Djordje M Kadijevich
email: djkadijevic@ipi.ac.rs

Cultivating Computational Thinking through Data Practice

Keywords: Computational thinking, data modelling, interactive charts, K-12 education, teacher education

After summarising the research context regarding defining, cultivating, and assessing computational thinking (CT), this theoretical paper examines data modelling using interactive displays, a CT practice that may be cultivated across several school subjects. Although working with data is not explicitly mentioned in some CT definitions, this work may activate different CT components, such as abstraction, decomposition, and pattern recognition. Furthermore, interactive displays, which are primarily a means for visualising data, can also be tools for modelling purposes if used within a modelling cycle. Focusing on this modelling in secondary education, we first consider main activities and their underlying skills, and outline what kind of support should be given to modellers, especially novices, in assisting them to complete this as easily as possible. We then consider what computational environment to use, which learning path to follow, and what assessment of learning to apply. Implications for teacher professional development are included.

Introduction

Today, education needs to prepare students to cope successfully with increasingly complex life and work environments, which often rely on technology (i.e. on automated computations). Because of that, following Wing's account of computational thinking (CT) as one of the basic student abilities [START_REF] Wing | Computational thinking[END_REF], many studies have dealt with CT in primary and secondary education across a number of school subjects by using various cultivation means. It seems that, in doing so, CT has not been intended to replace other contemporary approaches (e.g. problem-solving, critical thinking, creative thinking), but rather to complement and strengthen them by using concepts, tools, and techniques from computer science (e.g. [START_REF] García-Penalvo | Exploring the computational thinking effects in preuniversity education[END_REF]). As a result, students will be more than just technology-literate [START_REF] Yadav | Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classroom[END_REF].

CT was originally used to denote thinking processes applied in problem-solving to formulate solutions in such representations that could be efficiently processed by computers [START_REF] Wing | Computational thinking[END_REF]. It was viewed as an important literacy of the 21 st century, which would, to some extent, enable everyone (all learners) to: recognise aspects of problems amenable to computation; match those aspects to appropriate computational supports (concepts, tools, techniques, strategies); understand the opportunities and limitations of those supports; apply the supports in adapted or novel ways; and use computational strategies (e.g. a top-down approach) in any domain [START_REF] Wing | Research notebook: Computational thinking-What and why?[END_REF]. On the other hand, regarding scientists, engineers and other professionals, it was supposed that CT would enable them to reformulate problems to be (more) amenable to computation, develop and use new computational methods, ask and answer questions that rely on large data sets or intensive computations, and use computational terms to explain problems and solutions [START_REF] Wing | Research notebook: Computational thinking-What and why?[END_REF]. It can thus be said that CT may, in general, be viewed as a process whereby we recognise aspects of computations in our surroundings and deal, at introductory or advanced levels, with various systems and processes in these surroundings by applying tools and techniques from computer science [START_REF]Shut down or restart? The way forward for computing in UK schools[END_REF].

Increasing societal reliance on technology and data calls for connecting CT and data practice in the classroom. In the rest of this theoretical contribution, we first summarise the research context regarding defining, cultivating, and assessing CT. We then examine data modelling in secondary education by using interactive displays. Focusing on this, we first consider main activities and their underlying skills, and outline what kind of support should be given to modellers, especially novices, in assisting them to complete the modelling as easily as possible. We then consider what kind of computational environment should be used (one in which CT components may additionally be fostered), what learning paths could be followed in doing that, and how modellers' progression along this path might be assessed. The paper ends with implications for teachers' professional development.

Research context

Defining CT

Various definitions of CT have been proposed in the literature (e.g. [START_REF] García-Penalvo | Exploring the computational thinking effects in preuniversity education[END_REF][START_REF] Voogt | Computational thinking in compulsory education: Towards an agenda for research and practice[END_REF][START_REF] Shute | Demystifying computational thinking[END_REF]). Although the term has been used broadly, there has been no widely accepted definition so far [START_REF] Mouza | Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK)[END_REF]. For some researchers, from a general perspective, CT is concerned with algorithmic thinking, critical thinking, problem solving, and working cooperatively [START_REF] Doleck | Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: Exploring the relationship between computational thinking skills and academic performance[END_REF]. For some others, core CT facets are abstraction (data collection and analysis, pattern recognition, modelling), decomposition, algorithms (algorithm design, parallelism, automation), iteration, debugging, and generalisation [START_REF] Shute | Demystifying computational thinking[END_REF]. Other researchers, looking at K-12 education, assume that CT is a critical component of problem-solving supported by technology [START_REF] Barr | Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?[END_REF][START_REF]International Society for Technology in Education: Computational thinking. Teacher resources[END_REF], and propose concepts, such as data collection, data analysis, data representation, problem decomposition, abstraction, algorithm and procedures, automation, parallelisation, and simulation as core ideas. It seems that the main goal behind the request to cultivate CT in K-12 education is to prepare students to use computational tools in productive and creative ways within different school subjects [START_REF] Voogt | Computational thinking in compulsory education: Towards an agenda for research and practice[END_REF].

Apart from general frameworks, CT has been examined within subject-specific frameworks. For example, in the context of programming with Scratch, Brennan and Resnick [START_REF] Brennan | New frameworks for studying and assessing the development of computational thinking[END_REF] applied a CT framework with three dimensions, namely: CT concepts (e.g. data, operators, loops), CT practice (e.g. abstracting, modularising, debugging), and CT perspectives (e.g. questioning, connecting). In the STEM (Science, Technology, Engineering and Mathematics) context, focusing on high school mathematics and science education, a CT definition was given in the form of a taxonomy comprising four main practice categories [START_REF] Weintrop | Defining computational thinking for mathematics and science classroom[END_REF]: data practices (e.g. collecting, visualising), modelling and simulation practices (e.g. building and using computational models), computational problem-solving practices (e.g. programming, troubleshooting), and system-thinking practices (e.g. defining systems, managing complexity). Another general CT framework [START_REF] Kotsopoulos | A pedagogical framework for computational thinking[END_REF], exemplified for mathematics pedagogy, comprises four overlapping activities with various objects (of digital, tangible, or conceptual nature). These activities are: unplugging (not using computers), tinkering (taking objects apart and changing/modifying their components), making (constructing new objects), and remixing (appropriating of objects or their components to use them at other places or for other purposes).

Clearly, a standard definition of CT is lacking. However, because of its pedagogical utility, it seems promising to define CT using various CT practices and activities, examined in terms of underlying CT concepts and skills (extrapolated from the NRC [START_REF]Next generation science standards: For states, by states[END_REF]). Although working with data is not explicitly mentioned in some CT definitions, one CT practice, namely data practice [START_REF] Weintrop | Defining computational thinking for mathematics and science classroom[END_REF], should be included, since it may activate a number of CT components (e.g. abstraction, decomposition, and pattern recognition). Not only was the relevance of this practice for CT development (in particular, of data collection, representation, and analysis) recognised by the Computer Science Teacher Association (CSTA) and International Society for Technology in Education (ISTE) [START_REF]International Society for Technology in Education: Computational thinking. Teacher resources[END_REF], but work with data has also been included in an international assessment of students' computer and informational literacy, which assumed that, apart from programming, the CT domain deals with structuring and manipulating data sets as well (for more details, see https://www.iea.nl/icils).

Cultivating and assessing CT

Despite a relevant educational goal "CT for all" (initiated by Wing [START_REF] Wing | Computational thinking[END_REF]), our knowledge of how to integrate CT in K-12 education is still in its infancy, because research on integration is scarce [START_REF] Voogt | Computational thinking in compulsory education: Towards an agenda for research and practice[END_REF]. However, teacher education may, for example, benefit from examining examples of the use of CT in daily life. In other words, CT concepts (e.g. algorithm, abstraction, debugging) may be illustrated with concrete examples from teachers' daily experiences [START_REF] Yadav | Computational thinking in elementary and secondary teacher education[END_REF]. This approach, basically exemplifying various CT activities, has, for example, been applied by CSTA and ISTE [START_REF]International Society for Technology in Education: Computational thinking. Teacher resources[END_REF]. If teacher education is based on the framework of technological pedagogical content knowledge [START_REF] Mouza | Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK)[END_REF], the main focus should be on developing knowledge of CT-related concepts, tools, and practice (technological knowledge) and combining them with disciplinary content (i.e. content knowledge) and pedagogical strategies (i.e. pedagogical knowledge). Additionally, to promote appropriate CT within specific subject domains, teachers should be encouraged to avoid using just a few tools (e.g. concepts mapping tools, interactive whiteboards) and CT concepts and practices (e.g. automation, problem decomposition).

Research also evidences that, in general, we should cultivate CT within rich computational environments (in different domains such as game design and development, and with various CT instances such as abstraction and automation), and, in doing so, apply a use-modify-create learning path [START_REF] Lee | Computational thinking for youth in practice[END_REF]. Of course, having in mind different school subjects or university courses, CT practice should support or empower relevant scientific practice involving disciplinary knowledge and skills.

Although CT may be promoted through activities without the use of computers (e.g. CS Unplugged [START_REF] Bell | CS unplugged: An enrichment and extension programme for primary-aged students[END_REF]), the use of computing tools is nevertheless indispensable as they help learners test and revise their solutions involving CT concepts and practices (i.e. CT is primarily promoted through problem-solving with computing tools [START_REF] Barr | Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?[END_REF]).

Regarding CT assessment, there seems to be a vacuum in measuring and assessing CT achievement, which makes it difficult to judge the effectiveness of CT-based instruction [START_REF] Román-González | Which cognitive abilities underlie computational thinking? Criterion validity of the computational thinking test[END_REF]. In particular, because a standard definition of CT is lacking, measurements of this construct are diverse, which, as Shute and colleagues [START_REF] Shute | Demystifying computational thinking[END_REF] underlined, not only raises questions regarding results obtained, but also makes them difficult to compare. These researchers also stressed that assessing CT in classrooms is challenging and that to support a teacher's instruction, real-time assessments that monitor students' progress may be required.

Having in mind Brennan and Resnick [START_REF] Brennan | New frameworks for studying and assessing the development of computational thinking[END_REF], appropriate assessments could be based on the analysis of students' project portfolios (involving artifact-based interviews with them), assuming that novice students progress in developing projects along, in our terms, an understand-debug-extend trajectory (i.e. from understanding a developed project via debugging this project to extending it). With more experienced students, a use-modify-create learning path [START_REF] Lee | Computational thinking for youth in practice[END_REF] might be applied and assessed. To assess instruction that promotes CT among students by using computational tools in conjunction with content and pedagogy, we might use a technology integration rubric, whose criteria, as in [START_REF] Mouza | Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK)[END_REF], evaluate choosing and using tools and practices with respect to curriculum goals and instructional strategies, simultaneously aligning content, pedagogy, and technology.

Data practice using interactive displays

Despite the fact that a standard definition of CT is lacking, data practice, data analysis, or work with data can, as already mentioned, be recognised in a number of CT definitions (e.g. [START_REF] Shute | Demystifying computational thinking[END_REF][START_REF]International Society for Technology in Education: Computational thinking. Teacher resources[END_REF][START_REF] Weintrop | Defining computational thinking for mathematics and science classroom[END_REF]). Even when work with data is not mentioned explicitly in a CT definition (e.g. as in Google's main CT elements: decomposition, pattern recognition, abstraction, and algorithm design; https://youtu.be/sxUJKn6TJOI), it is clear that, for example, pattern recognition, dealing with regularities and trends in data, are based on data practice, which may make use of suitable technology, such as interactive displays.

Interactive displays and their educational relevance

Interactive charts are digital devices for the visual presentation of data, whose content updates automatically after changes in considered data or variables. Interactive displays are digital artifacts comprising one or more such charts, possibly coupled with other interactive reports, such as tables or summary measures. Interactive displays composed of two or more interactive reports, usually interactive charts, are called dashboards.

Typically built in a drag-and-drop fashion, interactive charts can be, as a descriptive, exploratory tool, (relatively effortlessly) used to visualise regularities and trends in data, if any. Several interesting interactive charts may, for example, be found at https://www.dur.ac.uk/smart.centre/. The application of these charts, especially for dashboards (typically also built in the drag-and-drop fashion), has increased considerably in recent years (e.g. [START_REF] Wexler | The big book of dashboards: Visualizing your data using real-world business scenarios[END_REF]; visit https://www.idashboards.com/dashboardexamples/, to view dashboards concerning various industries and areas). Learning analytics is, for example, one domain in which dashboards are widely used (e.g. [START_REF] Webb | Challenges for IT-enabled formative assessment of complex 21st century skills[END_REF]).

Because of such widespread and increasing use of dashboards, as well as possible learning and professional benefits, it is not surprising that there has been noticeable demand recently for the introduction of work with data using interactive displays in secondary education (e.g. [22 -26]). Although this work has traditionally been associated with data analysis, possibly based upon complex mathematical and statistical models, it is unlikely that most students would be required to perform such analyses in their future jobs. They would rather do some basic data modelling using dashboards, whether produced by others or resulting from their own modelling, to support their professional claims and actions (e.g. "peer feedback has been used by less than one-third of e-learners"; "another drug dose must be administered to that patient"), which may particularly be relevant to the STEM disciplines [START_REF] English | STEM education K-12: Perspectives on integration[END_REF]. 1 This modelling just makes use of simple mathematical models (e.g. frequencies, sums, and means) connecting independent and dependent variables; each model is, after the developer's chart selection, automatically applied by the tool used. Although interactive displays are primarily a means for visualising data, they can also be tools for modelling purposes if used within a modelling cycle.

Considering data practice through modelling

Knowing that even simple data preparation (e.g. querying datasets, (re)organising data) may be quite challenging for novice modellers [START_REF] Kadijevich | Data modelling with dashboards: Opportunities and challenges[END_REF], data to model (with just a few variables) should be given to them. In that case, data modelling may only require them to complete three main activities, namely: asking questions; visualising data; and answering questions. In other words, there may be just three key stages in the modelling cycle, usually advanced in a nonlinear way. For experienced data modellers, remaining activities could eventually be added: validating modelling (recommending changes) after answering questions; and preparing data after asking questions [START_REF] Kadijevich | Data modelling using interactive charts[END_REF].

To attain successful realisation of these activities, teachers need to identify their main underlying skills, and provide modellers, especially novices, with support to complete such modelling as easily as possible. Some of these underlying skills are, for example: choosing relations to examine; identifying dependent and independent variables (asking questions); selecting charts to use; selecting measures to apply (visualising data); recognising regularities in charts produced; and connecting regularities observed with corresponding questions asked (answering questions).

These and other underlying skills should be fostered through suitable scaffolding, taking into account potential modelling challenges and reasons for these challenges (discussed elsewhere [START_REF] Kadijevich | Data modelling with dashboards: Opportunities and challenges[END_REF], for example). As the three activities depend on each other, many scaffolds would connect their underlying skills (e.g. variables selection with charts production; charts production with regularities recognition). Of course, when interactive charts, especially dashboards, are created from scratch, data modelling becomes a design task, whose central activity of problem structuring [START_REF] Jonassen | Toward a design theory of problem solving[END_REF] needs meticulous scaffolding, possibly with (special) attention paid to the role of context in doing so. To ease contextual challenges, the data to model may be coupled with a short description of the underlying context. Furthermore, modellers may be scaffolded to develop problem structuring skills (e.g. selecting variables, measures, table, and charts to use) while improving knowledge of context under scrutiny (e.g. clarifying what other issues to examine, how they have been measured, and for what points in time to use data available), and vice versa (derived from [START_REF] Restrepo | Problem structuring and information access in design[END_REF]).

Performing and assessing data practice through modelling

As underlined above, CT should be cultivated within rich computational environments [START_REF] Lee | Computational thinking for youth in practice[END_REF]. Regarding dashboards, Zoho Analytics is, for example, such an environment (https://www.zoho.com/analytics/). 2 Apart from using and combining various interactive reports, it supports data preparation by querying relevant datasets. It also enables collaborative work on dashboard projects, which, if skillfully managed (e.g. in designing, building and combining charts of increased structural complexity), would promote other valuable CT assets, such as computational strategies. strategy) of an issue under consideration (e.g. overnight stays in a country). This strategy is clearly applied in building each individual chart concerning its elements, especially variables. Other strategies are top-down and bottom-up approaches. When we go from a dashboard as a whole to its individual reports as parts, a top-down approach is applied. When we start from some individual reports and combine them to create a dashboard, a bottom-up approach is used. (Instead of a single approach, we often apply a combination.) Furthermore, to get feedback and validation from peers and future users, building a chart or dashboard may make use of rapid prototyping, i.e. an iterative process through which we incrementally show what this display will look like. If we apply this interactive process to a chart, we may go from two variables (overnight stays by year) via three variables (overnight stays by year and country of residence) to four variables (overnight stays by year, country of residence and region, with values of the last variable used as a filter). In that way, we can examine whether the pattern of total overnight stays by year (the number of these stays is rather stable between six and seven millions in each year) holds true when we compare domestic and foreign guests, especially when we go from region to region.

As the dashboard development examines calls for simple system engineering, this development would only promote a basic understanding of these computational strategies. For a complex system, a top-down strategy could first be applied to identify its major units, followed by the main components of each unit. This strategy would then be repeated until the system is fully understood from top to bottom [START_REF] Faulconbridge | Systems engineering practice[END_REF]. Note that contrary to decomposition, the three other strategies, especially rapid prototyping, have been under-represented in CT-related research (exceptions can be found [START_REF] Kong | A framework of curriculum design for computational thinking development in K-12 education[END_REF][START_REF]Teaching computational thinking in primary education[END_REF]; cf. CT facet named iteration [START_REF] Shute | Demystifying computational thinking[END_REF]). For all these strategies to be put into practice, the use of a computing tool is indispensable, although some preparations for applying them may be done in an unplugged, paper-and-pencil environment.

Which learning path could be used to promote data modelling using interactive displays, and how may progression along this path be assessed? For work with digital artifacts in rich computational environments, Lee and colleagues [START_REF] Lee | Computational thinking for youth in practice[END_REF] proposed applying the use-modify-create path (e.g. for our data practice, from playing with developed displays via modifying developed displays to creating displays from scratch). If we combine this path with the understand-debug-extend learning trajectory (i.e. from understanding a developed project via debugging this project to extending it [START_REF] Brennan | New frameworks for studying and assessing the development of computational thinking[END_REF]), we may arrive at the following learning path: use displays to understand or evaluate data modelling done -modify displays to debug or extend data modelling realised -create displays to perform full data modelling by yourself (cf. Dagstuhl perspectives, [START_REF] Brinda | Education in the digital networked world[END_REF]). The evaluation of modellers' collaborative work may, as in Brennan and Resnick [START_REF] Brennan | New frameworks for studying and assessing the development of computational thinking[END_REF], be based on analysis of students' project portfolios, involving interviews with them about displays they have just evaluated, improved, or fully developed. To achieve this end, a rubric may be used with criteria that evaluate the success of pursuing each data modelling activity, making connections among them (e.g. in terms of major skills underlying these activities and links among them).

Closing remarks

To empower learners for life in the digital age, various digital practices should be mastered. One of them, suitable for many (most) learners, especially in vocational education, is the data practice presented in this paper.

Although this practice is linked to CT in a mathematical context (and such studies are quite rare [START_REF] Hickmott | A scoping review of studies on computational thinking in K-12 mathematics classrooms[END_REF]), it may, embedded in other contexts with different learning cycles, contribute to the learning of computer science or statistics. For example, an extended context could ask for the preparation of data through a CT-based work with databases [START_REF] Juxiang | Taking database design as trunk line of database courses[END_REF], whereby this practice becomes more relevant to computer science education where CT has become a critical component [START_REF] Webb | Computer science in K-12 school curricula of the 21st century: Why, what and when?[END_REF]. Or, instead of a modelling cycle, a data inquiry cycle could be used [START_REF] Gould | An analysis of secondary teachers' reasoning with participatory sensing data[END_REF], making this practice relevant to statistics learning as well.

Apart from its relevance to developing CT, our approach seems to focus on CT pedagogy. Through considering a range of disciplines, it was recently proposed that pedagogical CT environments should primarily focus on interactive visualisations or simulations, modelling and troubleshooting of data sets, and searching for patterns in large data sets [START_REF]Report of a workshop of pedagogical aspects of computational thinking[END_REF]. Clearly, our approach aligns with this focus.

To attain successful implementation of our approach in classrooms, professional development may primarily support teachers in realising and making connections between key data modelling activities in terms of their underlying skills (extrapolated from Niess and Gillow-Wiles [START_REF] Niess | Expanding teachers' technological pedagogical reasoning with a systems pedagogical approach[END_REF]), being aware of potential challenges in this modelling and reasons for these challenges (e.g. [START_REF] Kadijevich | Data modelling with dashboards: Opportunities and challenges[END_REF]). To avoid some of these challenges (e.g. concerning data preparation and context understanding), teachers may be supported in preparing materials that will be given to students (e.g. data to model and short context descriptions). Detailed support may be provided for them to prepare scaffolds that would help students develop problem-structuring skills while improving context understanding, and vice versa. Professional development may also support teachers in applying particular learning paths and assessing their outcomes (e.g. use displays to understand or evaluate data modelling completed -modify displays to debug or extend data modelling done -create displays to perform full data modelling by yourself) ( [START_REF] Brennan | New frameworks for studying and assessing the development of computational thinking[END_REF][START_REF] Lee | Computational thinking for youth in practice[END_REF]).

Apart from teachers and their appropriate professional development, successful implementation of our approach to data practice requires the support of other stakeholders (e.g. school administrators, policy makers, curriculum and assessment developers), who would eventually bring CT into classrooms and have it widely and skillfully applied (e.g. [START_REF] Weintrop | Defining computational thinking for mathematics and science classroom[END_REF]). However, to support the claims that particular CTinterventions are beneficial to students, we need to improve assessment to provide solid evidence that specific CT components/facets were indeed promoted [START_REF] Shute | Demystifying computational thinking[END_REF].

Figure 1 .

 1 Figure 1. Dashboard with two interactive charts and one summary measure: although not dominant, overnight stays by foreigners are increasingTo illustrate some of these strategies, let us consider a dashboard presented in Figure1. Its design may be the result of decomposition (i.e. a divide and conquer

Job candidates with data practice skills (in particular data science and analytics skills) will soon be preferred by most employers in the United States, for example (see http://www.bhef.com/sites/ default/files/bhef_2017_investing_in_dsa.pdf, for reported figures).

Acknowledgments. This contribution resulted from the author's work on the project "Improving the quality and accessibility of education in modernization processes in Serbia" (No. 47008), financially supported by the Serbian Ministry of Education, Science, and Technological Development (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018). The author dedicates the contribution to his son Aleksandar.


Mary Webb
email: mary.webb@kcl.ac.uk

Megan Tracey

William Harwin

Ozan Tokatli

Faustina Hwang

Natasha Barrett

Chris Jones

An Investigation of the Impact of Haptics for Promoting Understanding of Difficult Concepts in Cell Biology

Keywords: Haptics, virtual reality, cell biology, science learning

This paper reports on a study which investigated whether the addition of haptics (virtual touch) to a three-dimensional (3D) virtual reality (VR) simulation promotes learning of key concepts in biology for students aged 12 to 13 years. We developed a virtual model of a section of the cell membrane and a haptic-enabled interface that allows students to interact with the model and to manipulate objects in the model. Students, in two schools in England, worked collaboratively on activities, in pairs, designed to support learning of key difficult concepts. These concepts included the dynamic nature of the cell membrane, passive diffusion and facilitated diffusion. Findings from observation of the activities and student interviews revealed that students were very positive about using the system and believed that being able to feel structures and movements within the model assisted their learning. Results of pre-and post-tests of conceptual knowledge showed significant knowledge gains but there were no significant differences between the haptic and non-haptic condition.

Introduction and background

Haptics provides the additional sense of touch to a virtual reality (VR) environment, thus enabling people to feel the objects they are interacting with. Furthermore, a hapticenabled interface can enable people to directly manipulate objects in a threedimensional (3D) VR environment much more realistically than is possible through more standard interfaces such as mouse and tracker ball. Our research objectives were to: 1) design and develop a haptic-enabled VR environment that would enable students to explore difficult concepts through multisensory collaborative activities; 2) investigate whether or not the ability to feel the interactions through touch affected students' development of understanding of key concepts; and 3) examine students' perspectives on the interactive learning experience.

We focused on cell biology as an important area of science learning that poses significant challenges that may be addressed through the use of VR simulations.

Design of the VR environment

Designing the VR environment presented several challenges. First, cell membranes and the ways in which they control the movement of substances into and out of cells are very complex, so achieving a realistic model, for example by using real images, was impossible. Therefore, it was necessary to identify suitable iconic ways of representing structure and function. Understanding cell membrane function is crucial for understanding both the normal functioning of cell and organ systems as well as the opportunities for bio-engineering, and is an area of active research [see, for example, 13]. However, determining what level of understanding of such complex systems would be desirable and achievable for the target group of students presented a challenge. Furthermore, understanding of the functioning of cell membranes is an area of cell biology that leads to significant student comprehension problems and misconceptions, as explained earlier [START_REF] Flores | Representation of the cell and its processes in high school students: An integrated view[END_REF][START_REF] Malińska | Teaching about Water Relations in Plant Cells: An Uneasy Struggle[END_REF]. It is likely that some of these problems and misconceptions result from poor models and representations currently used for teaching. Therefore, key considerations for the design of the model included:

1. Identifying a level of complexity that would be sufficiently accurate not to lead to misconceptions, while being feasible to be modelled in a virtual environment and not too complex for students aged 12 to 13 years to understand. 2. Deciding on the level of detail for representing molecular structures that would enable understanding of their function in membrane transport. 3. Representing the relative size and scale of structures within a confined space, given that manipulation of the haptic-enabled interface is restricted to a limited vertical and horizontal space. 4. Modelling the haptic forces in such a way that students would be able to feel forces and manipulate the structures.

The screenshot in Figure 1 shows the cell membrane model near the start of the activity. The phospholipid bilayer of the membrane is depicted as a straw-coloured barrier with some hexagonal shapes representing the idea that the layer consists of many separate molecules and the bilayer is also indicated by the cross-sectional view. However, the details of the bilayer were not considered to be important for students aged 12 to 13 years to understand. The pale cream structures penetrating through the membrane represent the membrane proteins; several different types of membrane protein were modelled. Carbon dioxide and oxygen molecules, represented by their coloured atoms, following the CPK (Corey, Pauling, Koltun) colouring convention, can be seen in Figure 1.

The user is able to interact with the system via two points of contact of the thumb and index finger on the same hand (either left or right) by means of a thimble device as shown in Figure 2. In the model, the fingers are able to move freely through the cell membrane but when the user grabs hold of an object in the model, such as a glucose molecule, if the haptics is enabled, the user feels the object and any forces acting on that object, such as those resulting from concentration gradients. In the non-haptic condition, the user interacts using the same interface, but the haptics is turned off in the software, so the user must rely on visual cues to grab objects. When the user makes contact with one of the substances, the "Label" changes to show the name of the substance (carbon dioxide, oxygen, glucose, sodium and potassium). When haptics is enabled, users can feel forces on the substances, depending on their concentration, as they push a molecule or ion. During the simulation, users can add more molecules and ions, thus changing the concentration gradient. Some of the membrane proteins are modelled as glucose transporters, based on the GLUT1 transporter, as far as its structure and function is known [START_REF] Deng | Crystal structure of the human glucose transporter GLUT1[END_REF]. When a user pushes a glucose molecule towards a glucose transporter, the user feels the force as the molecule is drawn into the transporter protein, and the model simulates the glucose transporter changing shape as it transports the glucose molecule through the membrane. As shown in Figure 2, students worked in pairs, where one student (the pilot) was immersed in the VR environment using the interface and the head-mounted display, while the other student (co-pilot) had the same view of the 3D environment on a standard computer screen. The pilot controlled the interaction with the VR environment while the co-pilot directed the activity by: reading the instructions and questions on the worksheet; controlling some aspects of the model through the keyboard; and writing the answers onto the worksheet. The design of this learning environment was informed by a series of investigations with previous prototypes and discussions with teachers and students [START_REF] Webb | The potential for haptic-enabled interaction to support collaborative learning in school biology[END_REF]. The following were the main design principles: 1. to focus students' attention on the haptic interaction and feel of the structures through the activities and questions; 2. to encourage students to learn collaboratively by discussing their ideas; 3. to encourage students to formulate their ideas precisely.

For the students, aged 12 to 13 years, who were the subjects in our study, their usual study of cell membranes included examination of cell preparations under the light microscope, where cell membranes appeared as a thin line stained with a dye, together with two-dimensional diagrammatic representations and teaching and discussion regarding how membranes function to control movement into and out of cells, specifically through diffusion, osmosis and active transport.

Based on considerations of the existing curriculum, activities were designed to develop understanding of the following key concepts: 1. The cell membrane is a barrier to the movement of some substances whereas others pass through freely. 2. Substances move in the cellular fluid by diffusion and some substances are able to continue moving by diffusion through the membrane. 3. The movement of substances that are able to freely diffuse depends on their individual diffusion gradients. 4. The cell membrane is a dynamic structure in which membrane proteins "float". 5. Carrier proteins enable the movement of some substances through the membrane by attracting a specific molecule and changing shape as the molecule passes through the channel of the transporter.

Research methodology

The study was carried out in a boys' school and a girls' school with students who were in their first term of Year 8 (aged 12 to 13 years). Both schools were independent and selective, so the students were of relatively high academic ability. Opportunist sampling was used, based on which students could be freed from lessons at the time of the study. Pairs of students were assigned randomly to the haptic or non-haptic condition in equal numbers, and the students were not informed of this difference. In all, data were obtained from 32 pairs of students: 16 pairs in haptic-enabled condition, and 16 pairs in non-haptic condition.

In line with ethical considerations, the purpose of the study was explained carefully to the students, and theirs' as well as their parents' consent for the data collection was obtained. As some students were only exposed to the non-haptic condition, in a followup study later in the academic year, we ensured that all students had the opportunity to experience the haptic condition.

A test of biology knowledge, based on the key concepts listed above, and incorporating 14 true/false questions, was administered before and after the activity. While students were undertaking the activity (approximately 40 minutes in duration) they were video recorded. Students were observed by members of the research team who made notes on how students engaged with the activities and with each other. The research team later reviewed the notes and videos in order to identify advantages and limitations of the system and interaction. Students worked through the activity using the worksheet without teacher support; technicians were on hand to deal with technical issues with the hardware and software. Following the activity, students were interviewed in pairs using a semi-structured interview of about 20 minutes duration that elicited students' perspectives on: using the VR system; carrying out the activities; and collaborating while using the VR system as well as their thoughts on their learning during activities. The interviews were transcribed and subjected to inductive coding and thematic analysis. In this paper, we focus on the value of haptics for learning by examining both students' knowledge gains and their perspectives on their learning and how learning may be supported by the activities and the system.

Results

Observations of students during activities showed that both those in the haptic and nonhaptic condition were engaged with the tasks and most pairs worked very well together to support each other in interacting with the system and answering the questions. The majority of the students had not previously used VR systems and therefore the experience was novel and exciting. Generally, students quickly became familiar with the system and were able to use it effectively. There were some technical problems, particularly with the thimble devices, so it was sometimes necessary to stop and restart the system in order to adjust the thimble devices. The technical problems were quickly resolved by technicians or the students themselves and generally did not interfere with the progress of activities. Table 1 shows the scores for the 14 true/false questions on the knowledge tests. There was no significant difference in the pre-test scores for haptic (M=5.50, SD= 2.03) and non-haptic (M=4.84, SD=2.16) conditions; t(62)=1.25, p=.22. There was also no significant difference in the post-test scores for haptic (M=8.31, SD=1.97) and nonhaptic (M=8.34, SD=1.43) conditions; t(62)=-.07, p=.94.

There was a significant difference (p<0.001) between the pre-and post-test results suggesting that students had improved their understanding by undertaking the activities. However, whether the participants were in the haptic or non-haptic condition did not affect the change in score (p= 0.23). While the overall scores on the test improved, incorrect answers remained, including, particularly, answers related to Key Concepts 3 and 4 (see Section 3).

All the pairs of students reported that they found the system generally easy to use. However, a significant minority of pairs, both in haptic and non-haptic conditions, said that they found difficulty in grasping the objects in the system. Nearly all the students reported that they enjoyed working in pairs in these activities and found learning collaboratively was well supported by the system.

All the students who experienced the haptic-enabled condition were positive about being able to feel the particles. They commented particularly that they believed they gained a better understanding through using the VR system and specifically through being able to feel, especially compared with more traditional methods of teaching and learning such as listening to the teacher or viewing static diagrams. For example, when asked what they liked about the system, one student commented: "I liked the touch about it, so I'd know what it feels like" and further when asked about the benefits for their learning: "Well, because

if you looking at it you can only imagine what it's like, so you don't actually know what's it's like for real"

Students who worked in the "non-haptic" condition were also positive about the system and valued the visual experience but also commented on the value of being able to grasp and move the objects. The students were not told that the haptics had been disabled and were asked the same questions as the experimental group, including questions about the feel of the model, e.g. in the interview they were asked: "do you think being able to feel the membrane and the particles virtually can help you learn better, and why?" Some of these students were well aware of the lack of feel and commented on this as a limitation of the system, e.g. "It was hard 'cause it kept asking the question how you feel, but I couldn't physically feel so I didn't actually understand what, like, the question..." However, many of the students did not comment on this lack of feel. Some found the fact that they could grasp objects with their fingers but not feel them quite strange, e.g. "We couldn't actually feel them in a way… It's really weird, it's like you can see you're moving something but you can't feel like actually sense that you're moving it." Others thought that they were somehow compensating by imagining the "feeling" of what they were seeing, e.g.:

"I didn't feel too much, actually, with the haptic feedback... But, I think it really will improve kind of being able to feel the resistance, cos you can kind of feel it in what you're seeing. But there probably just needs to be a bit more actual vibrations, for example, coming out of the system" (Student in non-haptic condition).

Overall, the students felt that being able to interact with a haptic-enabled VR system would support their learning.

Findings from the interviews, together with observations of the activities and videos, showed that the students: were engaged with the system; they worked well together in pairs to complete the tasks; enjoyed the activities; found the experience fun and interesting; and believed that they were learning. Furthermore, findings from the preand post-tests revealed that students had better understanding of the subject matter after undertaking the activities with the system. Therefore, we can conclude that the design of the model and interface and the associated activities were: suitable for the students; enabled collaboration; were engaging and motivating; and supported students in learning most of the key concepts.

Observations of the students while they were undertaking the activities as well as students' own perspectives revealed that turning off the haptic feedback generally did not inhibit the students from interacting with the system and carrying out the activities. We can therefore conclude that students were able to compensate for the lack of feel through visual cues. As there was no significant difference in knowledge gains between the haptic and non-haptic condition in this study, turning off the haptics so that students could not actually feel the objects appears not to have affected their learning of concepts. This finding needs to be interpreted with caution. It is possible that just being able to grab the objects with their fingers enabled students to explore the environment and hence to learn. However, it is also noteworthy that on the post-test, students still found difficulty with some concepts that we had expected would be supported by their experiences of haptic feedback. Some observations during activities suggested that students were not experiencing the full effects of the haptic feedback that we had expected. This may have been because they had difficulties grasping objects, owing to problems with the thimble interface. Alternatively, or in addition, the forces may not have been sufficiently strong for students to notice. A possibility is that the students did not notice haptic stimuli because the visual stimuli, which were novel and exciting, directed their attention away from the haptic stimuli. "Visual dominance" is a wellknown psychological phenomenon, which suggests that people are more likely to notice and respond to visual stimuli than those from their other senses. For example, even in experiments where participants were compelled to attend to a particular sensory stimulus, an irrelevant visual stimulus interfered much more with their response to an auditory stimulus than vice versa [START_REF] Lukas | Switching attention between modalities: further evidence for visual dominance[END_REF]. Lukas et al. explained these findings in relation to the theory of directed attention [START_REF] Posner | Visual dominance: An information-processing account of its origins and significance[END_REF], which claims that visual stimuli are not as automatically attention-capturing stimuli as other modalities, so people have to actively focus their attention towards visual stimuli, which therefore occupy more of their attentional resources. A recent review [START_REF] Wahn | Is Attentional Resource Allocation Across Sensory Modalities Task-Dependent[END_REF] revealed that a majority of studies suggest that attentional resources are distinct for visual and auditory sensory modalities. However, findings vary, and may be dependent on the type of response being investigated and the context of the study. In this review, consistent with the majority of recent studies, the emphasis was on visual and auditory rather than haptic. Furthermore, the studies included in their review were focused in the real world whereas there is evidence that people's perceptions of touch vary between the real world and other representations. For example, Gaffary et al. [START_REF] Gaffary | AR Feels than VR: Haptic Perception of Stiffness in Augmented versus Virtual Reality[END_REF] found, in an experimental study, that people's perceptions of a virtual piston differed between an augmented reality (AR) and VR environment. A recent review also shows that there are variations in multisensory perception with age and across different groups [START_REF] Jonas | Introduction to the Special Issue on Individual Differences in Multisensory Perception: an Overview[END_REF]. In summary, although we kept the other conditions of the investigation basically the same between the experimental and control groups, the interactions between students and the VR environment and between students in their pairs, provided many opportunities for variations that may have influenced learning. In further research, we will be conducting video analysis of the interactions and undertaking further experiments in order to examine these possibilities in depth.

Whether or not the haptic feedback is critical for students' learning, the addition of haptics to a VR system does provide a more complete and authentic experience. Furthermore, some students found the experience of being able to grasp objects without feeling them to be strange. Currently, the addition of haptics to VR systems presents significant technical challenges. Therefore, typically VR systems rely heavily on visual representation and haptics is generally lagging behind the visual in implementation, including in, for example, surgical applications, where there is evidence that incorporating haptic feedback would be valuable [START_REF] Enayati | Haptics in Robot-Assisted Surgery: Challenges and Benefits[END_REF]. Currently, haptic interfaces are relatively expensive, whereas the costs of VR systems with 3D visual interfaces are reducing. If, as expected, it becomes possible to provide relatively inexpensive haptic interfaces to VR systems, then it will be important to identify the relative learning benefits and issues associated with haptic feedback in various situations.

While this investigation was of necessity conducted outside of their normal lessons and no elements of the usual interaction with a teacher were included in the activities, it is expected that similar activities could be incorporated into normal classroom lessons. For example, a haptic activity might be incorporated into a circus of investigations, in which students move around from one activity to the next during a lesson. Such activities might include various investigations using VR and haptics as well as the standard laboratory cell studies.

Figure 1 .

 1 Figure 1. View of part of the cell membrane model

Figure 2 .

 2 Figure 2. Students using the system

Table 1 .

 1 Comparison of pre-and post-test results for the true/false questions

		Condition	Mean	Standard Deviation	N
	Pre-test Score	Haptic	5.5000	2.03200	32
		Non-Haptic	4.8438	2.15690	32
		Total	5.1719	2.10483	64
	Post-test Score Haptic	8.3125	1.97464	32
		Non-Haptic	8.3437	1.42805	32
		Total	8.3281	1.70949

Acknowledgments. The authors are pleased to acknowledge support for this work from the Leverhulme Foundation project '3D Learning in a Rich, Cooperative Haptic Environment'. We are also pleased to thank our colleagues on this project, Jon Rashid, Carleen Houbart, Phil James, Richard Fisher, and Simon Bliss as well as all the students who participated.


N Yiannoutsou
email: n.yiannoutsou@ucl.ac.uk

N Otero
email: nuno.otero@lnu.se

W Müller
email: mueller@md-phw.de

C Neofytou

M Miltiadous
email: miltos.miltiadous@st.ouc.ac.cy

T Hadzilacos
email: thanasis.hadzilacos@ouc.ac.cy

Hanging Pictures or Searching the Web Informing the Design of a Decision-making System that empowers Teachers to appropriate Educational Resources to their School's Infrastructure

Keywords: Educational scenarios, micro-activities, educational innovation

In this paper, we report work in designing a decision-making system that aims to support teachers in appropriating to their practice innovative scenarios that employ uses of information and communication technologies (ICT) in teaching and learning. To this end, we break down educational scenarios into micro-activities, and connect them to required and alternative infrastructure. We argue that micro-activities is a unit of analysis of educational scenarios that is compatible with the role of teachers as designers who select, decompose, combine, enact and revise different pieces of resources. This paper offers a reflective viewpoint on integrating ICT in existing scenarios and investigates how teaching objectives make use, or not, of the potential of digital technologies.

Introduction

Educational technology moves much faster than pedagogical innovation. This leads to the paradox of schools never having enough information and communication technology (ICT) while this very ICT is underutilised. Teachers willing to at least try out innovative technology-enhanced educational scenarios are often stopped by perceived lack of necessary equipment. However, the question "can I do this with my existing school infrastructure?" may be unnecessarily getting negative answers, as obvious and non-obvious substitutes exist. While most teachers know about open source alternatives to a piece of software they do not have, they may need to be told that a shared document (e.g., Google Docs, cryptpad) can play the role of an interactive whiteboard, thus representing a not-so-obvious replacement for a piece of hardware described as being essential in a learning scenario identified by a teacher as desirable.

In this paper, we present a systematic approach to answer the question "Can I do this (ICT-enhanced lesson) with my school's infrastructure?" We start with a structural analysis (break up) of the educational scenario, leading to a sequence (or web) of 'micro-activities', where alternatives with other equipment may exist for each. It is up to the teacher, and depends on the learning context and goals, whether each of these alternatives is an acceptable alternative or not. Our analysis is backed by an ontologybased knowledge base system that provides the means to propose alternative implementations of scenarios on the technical level, potentially allowing for more sophisticated inference mechanisms in the future.

Theoretical background

In early discussions about integration of ICT in education, availability of resources was one of the contextual forces impeding the use of digital technologies in the classroom [START_REF] Mumtaz | Factors affecting teachers' use of information and communications technology: a review of the literature[END_REF]. Today, the situation is very different, as teachers are exposed to numerous learning resources through platforms, be they open (PhET, i2geo, LeMill, Curriki, EduTags), from textbook or learning tool publishers, or more social network oriented (e.g., OpenDiscoverySpace, eTwinning, YouTube for Schools, Canvas LMS). While these platforms offer widely-available learning scenarios and, sometimes, reports of experiential use in different contexts, their current impact on schools and teachers remains low [START_REF] Mavroudi | Let Me Do It: Towards the Implementation of Sustainable Instructional Patterns[END_REF]. Our observation is that each of these contributions is quite isolated and the deployment within the school infrastructure is often inexplicit. Lack of infrastructure used to be, and in some cases still is, a problem for ICT integration [START_REF] Vrasidas | The rhetoric of reform and teachers' use of ICT: The rhetoric of reform and teachers' use of ICT[END_REF]. However, today, the problem of infrastructure has been transformed to an issue regarding type of infrastructure available and teachers' access to it, making infrastructure an issue of school and/or national policy [START_REF] Mumtaz | Factors affecting teachers' use of information and communications technology: a review of the literature[END_REF][START_REF] Vrasidas | The rhetoric of reform and teachers' use of ICT: The rhetoric of reform and teachers' use of ICT[END_REF][START_REF] Tondeur | Curricula and the use of ICT in education: Two worlds apart[END_REF].

The paradox of choice (i.e., more is less) that applies in the availability and use of resources has another facet, which is related to the grainsize of resources available, and the way teachers use these resources [START_REF] Gueudet | From Text to "Lived" Resources[END_REF]. A fully-fledged scenario (lasting several hours) or a lesson plan, may be difficult to implement in another classroom for reasons related not only to curriculum and context (i.e., classroom, school, country), but also to a teacher's personal epistemologies and pedagogies (such as factors influencing the use of resources by the teachers [START_REF] Gueudet | From Text to "Lived" Resources[END_REF]). Furthermore, appropriation of this type of resources is often time-consuming and requires a lot of effort to overcome cultural, contextual and methodological barriers. This is not to say that a scenario or a lesson plan is not useful as a resource; instead, for the teachers to be able to use it, we argue that it is important to address teachers as designers, and not just as users.

These observations are backed by the work of Gueudet and Trouche [START_REF] Gueudet | Teachers' Work with Resources: Documentational Geneses and Professional Geneses[END_REF], which highlights that the use of resources by the teachers does not simply involve implementation of what they (the teachers) find available. Instead, it is a complex and demanding process involving a continuous dialogue between design and enactment. More specifically, teachers, using existing knowledge and influenced by the institution and the community they belong to, select resources, combine different pieces of resources together, test them in their class and revise the initial use (ibid). To capture this complexity, Gueudet and Trouche (ibid), describe the use of resources in practice as documentational genesis. Documentational genesis consists of two elements: a) the resource; and b) the development of a utilisation scheme. The latter involves a process of appropriation and transformation of the resource in order to solve a specific problem or to achieve a type of task (ibid). Documentational genesis is mediated by two intertwined processes. Instrumentalisation: where teachers appropriate and shape the resources (i.e., in our case educational scenarios/lesson plans) using their existing knowledge. Instrumentation: where a teacher's interaction with the resources (e.g., inspection, appropriation) enriches and shapes a teacher's knowledge and practice.

Our approach for the structural analysis of scenarios and the design of the recommendation system, is informed by the theoretical analysis of teachers' use of resources, in the following ways:

• We break up the scenario into micro-activities (which can lead back to the initial scenario) in order to facilitate the process of appropriation, selection and combination of different pieces of resources; • We provide connections of micro-activities to different types of infrastructures in order to facilitate the instrumentalisation process (i.e., adaptation of resources by the teacher); • We design recommendations for adaptation of micro-activities based on technology functionalities and different contexts of use, aiming to support the instrumentation process (enrichment of teacher knowledge). The purpose of the latter is to attend to the creative dimension of teaching and address teachers (also) as designers. Fischer et al. [START_REF] Fischer | Beyond binary choices: Integrating individual and social creativity[END_REF] highlight that creativity can emerge in contexts where people experience breakdowns (i.e., when they experience something they cannot do). Considering that our overarching question "Can I do this?" is also a fertile ground for creativity [START_REF] Nelson | Generating Transferrable Skills in STEM through Educational Robotics[END_REF], we do not provide ready-made solutions, but instruments to trigger a teacher's creativity, i.e., choosing one or more micro-activities from a scenario, showing how the same micro-activity can be transformed in different contexts and supporting the investigation of alternatives.

Motivation

The motivation for our work stems from two observations. First, the use of ICT in the classroom being tool-centred -as opposed to affordances-centred -very often results in short-sighted and trivial uses of digital technologies, which could be replaced by 'low tech' alternatives if seen from the point of view of the instructional goal they support. Second, looking into tool affordances -instead of specific pieces of softwarecan help facilitate the implementation of ICT scenarios with the available infrastructure, and also support teachers in harnessing the potential of ICT in the scenarios they apply in their classroom.

Based on these two observations, we argue that in order to answer the question "Can I do this" we need to adopt a critical stance both when we look at the uses of ICT in educational scenarios and when we look at the instructional goals underlying each activity. Next, we use the example of a simple activity, that of hanging pictures on the classroom wall, to demonstrate how focusing on the affordances of a tool and being critical of the instructional goals supported by the specific tool, can lead to a number of feasible (in terms of infrastructure available) and suitable (in terms of tool affordances) uses of technology.

Hanging pictures on the classroom wall -an outsider's view

We adapt an outsider's view to discuss the activity of hanging pictures on the classroom wall. Being an outsider that observes a classroom activity through the window of the class frees us from accepting contextual assumptions about the instructional goals, and directs us to explore the context by asking a very important question: why are they doing this? The exploration of possible answers to this question allows us to create a "locus of potentiality" populated with various instructional goals behind a single microactivity, each of which is re-examined in relation to the infrastructure it requires to be achieved. In our example, the micro-activity is the following: "The teacher asks the student to put up the picture on the class wall". The necessary infrastructure for this activity is: a) a framed picture; b) a hammer; and c) a nail. Now let us investigate "why are they doing this", i.e., what are the potential instructional goals behind this activity. In a kindergarten class, each pupil is asked to put a picture on the wall for the whole class to see everybody's work. In this context, the required infrastructure to perform this activity is shaped as follows: the picture does not need to be framed; pupils can use blue tack to put an unframed picture on the wall; hammer and nail are not necessary.

In a vocational education setting, the goal might be to show what type of hanging is suitable for each type of wall surface. In this case, the absolute specific infrastructure is needed (i.e., framed picture, nail and hammer), as they are essential for achieving the goal of the specific learning activity. A screw and a screwdriver might provide a useful alternative in terms of infrastructure, depending on the type of wall, or it could be used as a counter-example of what should not be done.

In a high-school classroom, the picture might be needed on the wall in order to analyse its content in a whole class discussion. In this case the goal is to make the picture visible to the whole class for the duration of the specific lesson. To achieve this goal, we might use a stone and a nail, instead of a hammer, a screw and a screwdriver if they are available, blue tack, or a computer and a projector. In this case the nail, hammer and framed picture are not essential.

In an examination context, at high school, the picture needs to be put up on the wall in order for the students to analyse it individually, responding to one or more test questions. In this case, the goal again is to make the picture visible to the whole class for the duration of the examination. All the solutions to replace hammer and nail mentioned in the previous paragraph are applicable here. Furthermore, considering the context of the examination, we might prefer to provide students with a printed picture allowing them to observe it closely and to comment on it in order to structure their response to the test. Alternatively, and if students have their computers or mobile telephones with them, they could access a common digital picture or slightly different pictures slightly changing the initial scenario. Again, nail, hammer and framed picture are not essential.

Method of work: Reverse engineering of educational scenarios

We mentioned earlier in this paper that our aim is to design a recommendation system supporting teachers to adapt existing scenarios to their classroom infrastructure. To this end we built a knowledge base consisting of 200 educational scenarios (accessible at: www.esit4sip.eu) drawn from the web and provided by the schools we are collaborating with. The next step was to select certain scenarios and ICT tools to focus on, in order to be able at a later stage to create a more general model to be applied in all the scenarios of the knowledge base. At this first stage, we chose scenarios of sufficient complexity based on teacher suggestions and on diversity of educational contexts. We analysed the selected scenarios using qualitative research methods and tools as to how and why ICT is used in each one. For this, we broke down the scenarios extracting ICT microactivities, which are smaller units of learner and teacher ICT activity. The way ICT is used in a micro-activity corresponds to ICT tool affordances. The educational effect to which ICT is used in a micro-activity corresponds to educational functionality. For each micro-activity, we considered technological alternatives with educationally equivalent functionality.

Figure 1. Method of scenario analysis -example of micro-activity with whiteboard

As long as "Can I do this?" refers to whole educational scenarios, answering it remains very complex. By breaking up a scenario into micro-activities, we reduce the question to finding equivalent and alternative micro-activities using different ICT infrastructure. Educational equivalence depends on the exact context and learning goals. A stone can replace the hammer for driving the nail in the wall, except if the purpose of hanging the picture was the very use of the hammer. Another word processor can replace Microsoft (MS) Word for writing a text, except if the purpose of writing the text was learning the specific characteristics of MS Word 2016.

We would need to know the educational rationale of the micro-activity in order to find proper equivalents using alternative infrastructure. But the educational rationale may be hidden and certainly not explicitly stated in an educational scenario. What our system can do is discern patterns of use (instructional approaches) and propose alternatives for the teacher to decide if they are sufficiently suitable substitutes -some may be unacceptable, others may modify the learning results, others may be equivalent, still others may offer something quite different but quite acceptable. To better demonstrate our methodology, we present in the following the procedure we follow to generate recommendations:

1. From the scenario abstract micro-activities ("hang a picture") and related 'infrastructure' (hammer, nail, picture) 2. Consider diverse possible instructional rationales of each micro-activity (diverse educational contexts): "why would you want students to hang pictures on the wall?" 3. Analyse functionality/affordances of infrastructure -Hammer: can drive nails into walls; Nail: can hold framed pictures on walls; Picture on wall: can be seen by whole class 4. Consider other infrastructure with similar functionality (stone ~ hammer, screw ~ nail) or similar result (glue ~ hang, project digital ~ hang physical, directly observe single physical object ~ through ICT observe digital copies) Next, we show how we use these steps to analyse an ICT-based scenario in order to provide recommendations for equivalent and alternative activities for one of its microactivities (collect material).

"Searching the web": Analysis -recommendations for equivalent and alternative activities

The work we report here takes place in the context of the Erasmus+ project "eSIT4SIP" (empowering the School IT infrastructures for the implementation of Sustainable Instructional Patterns, www.esit4sip.eu). In section 4, we mentioned that part of the project's outputs is an ontology-based knowledge-base consisting of 200 scenarios. From this knowledge-base, we extracted the scenario: "How to revive the story" (Authors: Nada Stojičević, Nikola Ćurčin). The scenario is designed for 15-18-yearold students. The subject matter is not mentioned. Following the analytic scheme presented in section 4, our analysis of this scenario takes the following form:

Micro-activities: The micro-activities (coded as MA) extracted from the scenario are the following -MA1: Prepare a story that triggers student interest about QR codes and animated maps; MA2: Discuss the story with the students; MA3: Create groups of students (different roles: photographers, researchers, coordinators, animators, web designers); MA4: Taking pictures of selected sites (topics in the original); MA5: Collect material [interpretation: information for the locations] that will be integrated in the animated map; MA6: Create animated maps. [Subject: students]; MA7: Upload the finished materials to the site (Wordpress); MA8: create and print quick response (QR) codes [Subject: students]; MA9: Students present their work [Subject: students].

Infrastructure: The infrastructure mentioned in the scenario involves: at least 15 computers with internet access; mobile telephones with cameras and QR code scanner; digital photo cameras; software: Animaps, Wordpress, Panorama, QR code generator.

Functionalities/affordances of infrastructure: Web-search: Find information around a subject using relevant keywords, offer access to various types of information regarding the topic of interest and sort the information found from the most relevant to the least relevant.

Other infrastructure. Similar result: take interviews, use information from an accredited source. Similar functionality: use teacher laptop and projector instead of computer laboratory.

To demonstrate our approach, we will focus only on the "Collection of information" micro-activity (MA5). We chose this activity for two reasons. The first is that collection of information is a common element of a wide variety of scenarios, especially in social sciences and humanities. The other reason is that information seeking is part of a new set of skills acknowledged as digital competences [START_REF] Vuorikari | DigComp 2.0: the digital competence framework for citizens[END_REF]. In the next two sub-sections, we show how we generate recommendations for alternative and equivalent activities based on an analysis of the affordances of technology (i.e., what is its potential) in relation to the teaching objectives it serves (i.e., what a teacher would intend to do with this technology).

Diverse instructional rationales (alternative activities)

The scenario mentions that students collect information to add to the interactive map without specifying the means or the type of the collected material. However, in the introductory session, the authors describe the actual output of students' work: "In addition to photography, the user will be reading an explanation of the museum building, centre, municipal building, park, church ....". Situating this in the context of the overall scenario -i.e., the creation of an interactive map -helps us to assume that the collected information will consist of short texts describing the sites of the map.

The scenario neither explains the means nor the tools students are going to use to collect information. Two assumptions are drawn from this: a) the use of web search is so widespread that it is not necessary to be mentioned; b) the type of information collected is not that important because the emphasis is on adding content (photographs, text) on the map. If we accept the first assumption, then web-search is a "legitimate" learning activity. In this case, the infrastructure required for this micro-activity is: computer laboratory, computers connected to the internet, browser. Next, we provide two examples of instructional goals related to the micro-activity involving web-search and respective settings.

Setting 1 -Instructional goal 1: Identify differences between search engines and between devices (owned by different users) -the filter bubble [START_REF] Pariser | The filter bubble: How the new personalized web is changing what we read and how we think[END_REF]. The infrastructure mentioned in the micro-activity is the same, but the instructional goal involves the development of digital competences. The micro-activity can be implemented with the same age group in the context of IT lessons.

Setting 2 -Instructional goal 2: Seek, evaluate, select and appropriate information from the web. Here the emphasis would be for the students to learn how to refine their keywords (seek information), evaluate the sources of the information provided and appropriate the information they select to their purposes. The micro-activity can be implemented across subject topics, with younger audiences (age 12-15 years), using the same infrastructure (as in the initial MA).

Infrastructure with similar functionality or similar result (equivalent activities)

In this section, we present how the focus on the technological affordances in terms of results and/or functionalities can support the implementation of the micro-activity "Collection of information" in the classroom with different types of infrastructure.

Infrastructure with similar result 1: Take an interview from someone who has the information or ask people who live or work next to the sites that are going to be included in the map, to describe the site of interest with a relevant representative phrase. The infrastructure needed here is a notebook or a sound recording device. From an instructional point of view, the use of this infrastructure will allow students to collect the information they need. This choice can be "instructionally acceptable" in the following situations: a) if the means students use to collect information does not really matter for the specific learning scenario (i.e. there are no implicit or explicit objectives regarding this activity); b) if the teacher would like to explore with the students the interview as a medium for collecting information. If a teacher decides to take this path, then he/she should envisage a time slot for the students to work on appropriating this information to be integrated in a digital map. A final step would be for the students to delve into the differences between the information collected and that existing in other digital maps. From an instructional perspective, this option might offer rich learning opportunities regarding the use of digital information, especially if compared to unstructured web-search where students type a keyword and copy-paste information from the first result coming up.

Infrastructure with similar result 2: The teacher provides each group with print-outs containing information about the sites they are going to include in their map. Their job is to appropriate this information so as to be interesting for the users of the map and to record it on a piece of paper. Infrastructure: printouts and notepad. From an instructional point of view this option can be acceptable if the teacher does not want, or does not have the time, to emphasise the aspect of including information on a digital map or wants her/his students to work on specific information, e.g., taken from an accredited text book. The appropriation of information, however, should consider the functionalities of a digital map (length and type of information shown). From an instructional point of view, this activity, though not making direct use of digital technologies, is done with reference to digital technologies (i.e., the functionalities of a digital map) and allows for focused work on the editing and appropriating information, which often is overlooked when simple web-search is involved.

Infrastructure with similar functionality: Students work in groups, each group being responsible for one site of interest, and take some time to think about the keywords they could use in order to search information on the web. When they are ready, each group takes turns in dictating their keywords to the teacher who types them on his/her laptop and the teams see the results through a video projector. Then the group, with the help of the teacher and the other groups, review the results and refine their search if necessary. The teacher goes through the information found (i.e., reading it aloud) and the group responsible for the specific site takes notes to use them for the construction of information to be included in the map. Alternatively, the teacher can print out the information for the groups to adapt it for the map. The infrastructure that is necessary here is one computer connected to the internet, a browser, and a projector. From an instructional point of view, the use of the same technology in a different orchestration (from the computer laboratory to the teacher laptop) makes salient in the whole class a process which usually happens in a group or individually. This transfer results in a qualitatively different process, as it offers opportunities for refinement from different viewpoints (i.e., the viewpoints of the other groups), which is rarely pursued when websearch is just a small step for something else (e.g., to use the information found to construct a map).

The analysis of this scenario aimed to show the application of our analytic framework and the production of recommendations by domain experts. The recommendations stemmed from a critical-reflective analytic approach on the uses of technology. This approach involved a focus on learning objectives and technology affordances. In the example we analysed above, we found that there are uses of ICT which do not harness the potential of digital technologies to support learning (in the sense that the same learning objectives can be pursued effectively without technology). Furthermore, looking at the technology from the point of view of affordances allows us not only to rethink the infrastructure needed but also to come up with new educational activities which rely on what the technology can actually do.

Concluding remarks

In this paper, we described our approach in empowering teachers to integrate available resources (i.e., ICT enhanced scenarios and lessons in their practice) by making use of existing infrastructure in their schools. Our work is informed by two theoretical underpinnings. One is the role of infrastructure in impeding teachers to try innovative educational scenarios in their class. The second involves the role of the teacher as designer. In order to facilitate the appropriation process, we break down the educational scenarios into micro-activities, each explicitly connected to the infrastructure mentioned or implied in the scenario and to other equivalent solutions. Furthermore, considering that the use of resources is also a creative process enriching and shaping a teacher's knowledge, we explore diverse instructional rationales around the use of infrastructure exploring alternative micro-activities. We used a quite general example to illustrate the implementation of this analytic framework and to show how this analysis can be used to inform the design of an ontology and a knowledge base supporting a decision-making system for teachers.

Currently, the decision-making system provides alternatives for the 200 scenarios of the knowledge base which are tagged manually according to parts of the analysis presented here. The implementation of the system at this stage looks only at the tools and their affordances in a scenario, and based on these affordances, provides suggestions for alternative tools. The next step is to refine our system so as to include the micro-activities, the educational functionalities and a set of technology-enhanced learning design patterns. The latter are recurring ICT-based solutions for recurring educational problems in diverse educational contexts, which will be used to conceptually group scenarios, thereby allowing integration of contextual knowledge in our system related to the use of the tools and not only on their affordances. The question "Is this alternative acceptable, i.e. does it serve the learning goals?" was put to teachers. It is the teacher of course who decides if the learning goals are met with the proposed replacements/substitutions. Seeing an active role for the teachers in this process, we expect to create a vibrant community with teachers and other stakeholders, sharing learning scenarios, providing advice regarding the implementation of the different scenarios, or even suggesting alterations based on their own experiences.

Acknowledgments. The research has been conducted in the context of the eSIT4SIP project, co-funded by the Erasmus+ programme of the European Union. The European Commission support for the production of this publication does not constitute an endorsement of the contents, which reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.


Matthias Ehlenz
email: ehlenz@cs.rwth-aachen.de

Thiemo Leonhardt
email: leonhardt@cs.rwth-aachen.de

Ulrik Schroeder
email: schroeder@cs.rwth-aachen.de

A Learning Analytics Approach in Web-based Multiuser Learning Games

Keywords: Game-based learning, learning analytics, multi-touch, educational games, learning context

As technology changes, learning games are adapted to target audience and available devices. Analytics methods must keep up with keeping the learner in focus. This work presents the Multi-Touch Learning Game (MTLG) framework, designed to implement cross platform educational games with support for cooperative, collaborative and competitive settings. It shows adaption of a user-centred learning analytics data model, the learning data context model, to fit circumstantial requirements of multi-user settings on a shared device in games implemented using the MTLG framework. A first field study has been conducted, and the results, challenges and lessons learned are discussed.

A new style of learning

Learning nowadays requires educators around the globe to understand current developments of everyday habits of their students to provide methods of knowledge transfer via alternative channels, for example, game-based learning. To avoid unnecessary overheads in development processes, the method of choice for producers is often web-based. Hypertext markup language (HTML) and JavaScript based content can be viewed on a range of devices and can even be compiled without much effort in native applications (apps) for nearly all the common target platforms. Though this is carried out on an everyday basis, most learning analytics standards do not reflect those different settings. This paper aims to fill the gap between context awareness in learning analytics and their application in game-based learning. This challenge is approached by extending the Learning Context Data Model to fit modern collaborative contexts of "playing together" and specifics regarding educational games, to offer a broad range of analytic possibilities in game-based learning.

The paper has three parts. First, we explain the Multi-Touch Learning Framework to integrate learning analytics in web-based educational games and applications. Second, we introduce the Learning Context Data Model as an alternative to the Experience Application Program Interface (API), as it focuses on the learner and not only on the learning event. Third, we present our first field study as a proof of concept.

Multi-touch learning games

Educational games play an important role in modern education. It is widely accepted that engaging games enhance learning in a wide variety of ways [START_REF] Hamari | Challenging games help students learn: An empirical study on engagement, flow and immersion in gamebased learning[END_REF]. As progress leads to a wide spread in platforms, developers are often either forced to stick with their area of expertise, thus limiting the reachable audience, gather an expensive team, or risk the time-consuming effort of getting accustomed to new areas. An alternative is to follow the more modern path of a cross-platform approach.

Most of those used on an everyday basis by the target audience of educational games, mainly children and teenagers, are not desktops or notebooks but tablets and smartphones are dominating this area [START_REF]Medienpädagogischer Forschungsverband Südwest mpfs: JIM-Studie 2017. Jugend, Information[END_REF]. Touch interaction has outrun keyboard and mouse and a big share of newly-introduced notebooks feature touch functionality. Thus, educational games make use of the possibilities of gestures and ten finger input that students have grown accustomed to. Furthermore, technology has advanced and produced touchscreens in growing sizes and capabilities. This opens up opportunities far beyond single learners behind their own devices and enables content creators to provide true collaborative learning environments where all users have per definition equal rights and chances of participation.

The Multi-Touch Learning Game framework (MTLG) developed by the Learning Technologies Research Group at RWTH Aachen University aims to assist university students, teachers and other developers to create suitable user experience on devices and for their demanding clientele, while maintaining core features of educational games [START_REF] Ehlenz | Spielend leicht Lernspiele entwickeln -Ein Framework für Multitouch-Lernspiele[END_REF]. In this paper, we focus on discussing the analytics component, as it represents the foundation of understanding behavioural patterns in multiuser multi-touch learning environments, and describe the challenges those collaborative settings bring.

Intention of the learning context data model

Learning analytics is a current trend, but is not a new and revolutionary topic. Since Learning Management Systems (LMSs) have been around, educators have striven to understand their learners and improve their systems to optimise user experience and, in the long run, maximise learning efficiency.

After early approaches to standardise a data format for interchange and storage of learning records like Tin Can API, the successor, Experience API (xAPI) has become a de-facto standard for learning record stores and is implemented in all major LMSs. xAPI represented a huge improvement compared to Tin Can regarding support of game-based learning, platform transitions and learning in teams, but still is mostly fitting for traditional e-learning scenarios. xAPI is a specification with the set main goal of interoperability in mind and providing a widely generic "one size fits all" solution which proves applicable in most settings.

Using xAPI to store the whole context of a user's learning environment stretches the bounds of this ambiguity beyond the point of good conscience, especially when interoperability is not the priority in the intended project. Thus, during the Prime Project, Thüs et al. [START_REF] Thüs | Mobile learning in context[END_REF] developed the Learning Context Data Model (LCDM), which is a learning record data definition aiming to provide a structural way to collect information not only on the learning process itself but also on the context of the learner [START_REF] Thüs | Kontexterfassung, modellierung und auswertung in Lernumgebungen[END_REF][START_REF] Thüs | Kontexterfassung zur Anreicherung mobiler Anwendungen[END_REF]. Furthermore, this model is not only platform agnostic, it provides a structured pattern for storing the platform, source, session, action and general learning setting (work, academic, private); it introduced a hierarchical category system, with entities to model user interests [START_REF] Thüs | Evolution of Interests in the Learning Context Data Model[END_REF] and generic entities.

The LCDM was implemented for usage in professional settings, but with the intention to allow self-reflection on daily tasks and learning activities [START_REF] Thüs | Smart Experience Sampling in Android[END_REF]. Thus, it is not an event-centric or application-centric data model, but focuses on the learner. This user-centric idea emphasises applicability of this data model in multi-user settings as context might play an important role in analysis of gathered data. For use in MTLG games, the LCDM has been ported by a Representational State Transfer (REST)-Interface using a simple token-based authentication system. During implementation, an adaptive REST endpoint was created to facilitate exports of all events in xAPI format to keep future opportunities for using analytics toolkits, relying on the said standard.

First field studies and challenges

As proof of concept and a first research prototype, a four-user learning game for the introduction of regular expressions (a central topic in theoretical computer science and foundation for formal languages), has been implemented as the first game using the data collector and the LCDM back end. The basic idea shown in Figure 1 is that each player has her/his own drop area, associated with a regular expression. In the common area of the screen, word cards appear, either matching (one of) those regular expressions or not. Players have to touch matching cards and drop them in their drop area. Correctly assigned cards are awarded two points, while mismatched cards reduce the score by one. Depending on the setting, there are either individual scores or a team score, individual expressions or the same for all players, and the play modes consist of two versus two and cooperative playing.

One goal of the study was to explore the practicability of the data model in real usage scenarios and investigate reliability of the produced data set. Information gathered for each touch interaction consisted of the result (success or not), drop area, regular expression on the drop area, timestamps and duration as well as session identifier (id).

Random sample checks comparing event data with the video recording of those sessions suggests that the data set is complete, and no interaction events have been lost. Nevertheless, there is a good amount of unassigned word card interactions since the first approach was guessing player id from the drop area, and cards moved in the common area could not be resolved that way, thus being assigned to the unknown player "John Doe" for later assignment in post-processing.

Another problem surfaced during observation, and shown by analysis of the visual recordings. Both in cooperative and competitive settings (less in the former, often in the latter), cards have been dropped in areas of different players, in the first case usually during the process of mutual explanation, and in the second, in taking advantage of the score malus for wrong cards for the opposite team for dropping those cards across the table. While this could be seen from a mostly negative perspective, some researchers [START_REF] Jabbar | Gameplay Engagement and Learning in Game-Based Learning: A Systematic Review[END_REF] indicate that such a change in behavioural pattern can be interpreted as emotional involvement and thereby immersion in the playing experience. Still, this renders the collected data effectively useless in an unprocessed state in the attempt of user-centric analysis and demands of approaches either in post-processing or future data collection.

While this first field study merely aimed, from the technological perspective, at showing the capabilities of the data collector and back end, it left us with valuable insights and optimism regarding the planned approaches on the expected challenges.

Regarding further field studies in laboratory conditions, post-processing in regard to user assignment will be rendered obsolete. The TABULA Project enrichens learning experiences with so-called tangibles [START_REF] Voelker | PERCs: persistently trackable tangibles on capacitive multi-touch displays[END_REF], objects that can be detected by any capacitive touchscreen device and adds a haptic aspect to the process.

While the users in our first case study have already been using the first version of passive tangibles for comparable user experiences, those tangibles are not distinguishable from each other. In the next, those tangibles will become active. By adding a microcontroller board with Bluetooth capabilities, the tool in the hands of each student will provide options like a direct and discreet feedback channel to each user, uniquely identifiable and linked (in the context of this prototype) to a specific user for the whole game session, which will empower us to assign "cross reaching" events as well as "unassigned" events to a specific user.

Conclusion and outlook

The first field study shows that the LCDM fulfils the needs of multi-user multi-touch learning games. There are adjustments to make to fit specific use cases, but mostly at a semantic level, like extending the range of values for a platform (by a MTTABLE value), or minor categories (by touch interaction events). Introducing a fourth category type GAME is in discussion. Software tools are currently being implemented enabling analysts to synchronise a recorded video with an LCDM event stream for easy, timeefficient post-processing for general study use without availability of tangibles.

The next iterations will collect an advanced set of interaction data including the start and end coordinates of each touch interaction, opening up the possibility to answer straightforward questions like "Does this user collect from the whole common area or just from within arm's reach" to sophisticated ones like "Does a user become more hesitant over [time/failed attempts/difficulty]?" by looking at pixel distance to duration ratios. Further research will be done, aiming to implement automated, personalised feedback, continuing and transferring the groundwork laid in a previous study [START_REF] Herding | The Tutor-in-the-Loop Model for Formative Assessment[END_REF].

Figure 1 .

 1 Figure 1. PoC Prototype (Source: Own work -http://tabula-content.informatik.rwthaachen.de/games/dem1-RegEx/)

Acknowledgments. The authors acknowledge the financial support by the Federal Ministry of Education and Research of Germany in the call ELE (Project TABULA).