Silvia Rita Viola

Elizabeth Da Lio

Monia Sannia

Giancarlo Ercoli

Tommasoleo

Lifelong learning for the Italian Public Administration: the MarchE-Learning Project

Keywords: e-learning, lifelong learning, active learning, collaborative learning, corporate learning, monitoring, evaluation

This paper describes a local experience for the development of a framework program to set up a Continuous Learning System for Public Administration employees of Marche Region, in Central Italy. Such program belongs to a greater national program, aimed at modernizing the Italian Public Administration by means of the application of ICTs. This program is also part of Italian participation in the European Union's Lisbon Strategy, aimed at turning Europe into one of the most competitive knowledge-based societies in the world.

The activity aims at setting up a network of reference PA workers in underprivileged areas of the Marche Region, and training them to detect needs that will be satisfied through a network infrastructure to carry out e-learning activities.

Introduction

In March 2000 the European Council met in Lisbon and launched the Lisbon Strategy [5], also known as Lisbon process or Lisbon Agenda. It deals with the transition to the so called "knowledge based economy". The Lisbon strategy aims strategically at:

• promoting innovation as the motor for future change,

• developing a learning economy,

• carrying out social and environmental renewal to overcome low productivity and stagnation of economic growth in the European Union. The Council stressed the importance of the adoption of new Information and Communication Technologies to reach those training and knowledge levels necessary for a competitive society for the third millennium. Following the Lisbon meeting, the European Union launched a set of initiatives to apply new technologies to training and education as well as to governance. The Barcelona European Council in 2002 [6] called on the Commission to draw up an eEurope action plan focussing on the widespread availability and use of broadband networks throughout the Union by 2005, the development of Internet protocol IPv6 and the security of networks and information, eGovernment, eLearning, eHealth and eBusiness. Accordingly, the Commission launched a number of programs sustaining projects to carry out pilot experiences and to define quality standards and benchmarks. This remarkable effort has seen European Academic Institutions, Public Administration Offices and private companies actively co-operating and one of the results has been the elaboration of syllabuses for definition and certification of competencies for on-line teaching [START_REF] Ulearn | The European Sylllabus for Pioneer Teachers[END_REF] and repositories of best practices.

Each country within the EU applied national strategies to put the agenda into practice. In Italy the Ministry of Innovation and Technologies in 2002 published its "Guidelines for the development of a knowledge society", declaring that training through e-learning was one of the ten goals of the Government plan. Concerning Public Administration the guidelines recommended the development of e-Government and the use of ICTs at the level of both central and local government. The guidelines even set a goal of carrying out at least one third of training activities for public administration personnel through e-learning by the end of 2005. In January 2004 the CNIPA (National Centre for Information Technologies in Public Administration) was set up to assist Public Administration institutions to develop forms of egovernment, manage and supervise communication technological structures used by the institutions and carry out training activities for the personnel. The CNIPA depends directly from the Council of Ministers and is the result of the fusion of pre-existing public organisations.

In the field of e-learning for the personnel the CNIPA has been very active and published a very accurate set of guidelines for the implementation of training activities in e-learning [START_REF] Cnipa | Linee guida per i progetti formativi in modalità eLearning nelle PA, I Quaderni[END_REF], and a Vademecum with detailed instructions concerning all phases of the development of an e-learning training project, from need identification and target definition to implementation, monitoring and evaluation [START_REF] Cnipa | Vademecum per la realizzazione di progetti formativi in modalità eLearning nelle PA, I Quaderni[END_REF]. In these publications CNIPA has given special attention to the organizational aspects of training, stressing the importance of motivation as a fundamental element in adult education. Thus CNIPA recommends the institutional official support to training activities, which should mean availability of time, space and structures as well as positive influence on the employee's career. On the other hand, CNIPA's work stresses the central role within institutions of selected and specifically trained employees who are able to identify training needs in order to improve the fulfilment of the institution's mission. In 2005 CNIPA has also set up a repository of learning contents to make possible for any Public Administration office the re-use of materials produced by the Centre or by other offices.

Motivations and Background

Because of its strategic importance within the so-called "Knowledge Society", Lifelong learning is an hot topic in technology enhanced learning research, and different projects have been developed worldwide. Thanks to its importance and pervasiveness, the topic has been developed across different Programs (higher education Programmes, training on the job Programmes, etc.). As examples, it is worth to remember all the European programs aimed at supporting lifelong learning initiatives, such as the Socrates, Leonardo da Vinci Programmes [START_REF]European Programmes for Lifelong Learning[END_REF], the European Social Fund (ESF), and the TEN Competence European network [START_REF]TEN Competence-The European Network for Lifelong Learning[END_REF].

The interest in lifelong learning is not limited to Europe and European Research Policies. As an examples, we can remeber the Taiwan National Science and Technology Programme of E-Learning [13] and the Cyber Learning Centre Plus [START_REF]Cyber Learning Center Plus[END_REF] for Hong Kong's public servants.

In Italy different Programmes have been launched for promoting lifelong learning. Just as examples, we can remember: the experience of ID (InteractiveDistance Learning) an elearning project promoted by the Italian Ministry of Justice with the purpose to establish a long-life learning platform based on the web technology to teach legal content to the Ministry employees [START_REF] Palmirani | longlife e-learning project in the Italian ministry of Justice, Database and Expert Systems Applications[END_REF]; the ESF funded TRIO Project [19], aimed at supporting lifelong learning of citizens and the training on the job in the Tuscany Region; the "Rete Interregionale Tecnologie per la Formazione (RITEF)" (Inter-regional network for education) [START_REF]Rete Interregionale Tecnologie per la Formazione (Italy)[END_REF]. Specific projects aimed at supporting lifelong learning of employees of Public Administrations have been also developed by using technology enhanced learning: we will remember the SELF project, promoted by the Emilia Romagna Region [START_REF]SELF -E-learning federato per la Pubblica Amministrazione (Italy)[END_REF]; the PATENT (Public Administration Training European Network for the Harmonization of Training Approach) Project [START_REF]PATENT -Public Administration Training European Network for the Harmonization of Training Approach[END_REF] and European Senior Civil Servant Initiative [START_REF]European Senion Civil Servant Initiative[END_REF]. All these Projects focus on different aspects of lifelong learning practices and technologies, such as the reusability of learning materials, the development and delivery of specific Courses, the development of learning networks, or the pedagogical and didactic efficiency and efficacy of technology enhanced learning. So far e-learning training activities are carried out, mainly using the asyncronous mode, to deliver specific courses on topics concerning information technologies and Public Administration innovation, without the creation of real comprehensive systems [START_REF] Mobilio | La formazione tramite le nuove tecnologie nei siti internet della pubblica amministrazione[END_REF]. Even at central level there are few public institutions that may count on an integrated ICT system for the management of training. In fact, the lifelong Projects and Programmes for PA follow in general a traditional pedagogical schema, in which the educational needs are firstly detected and/or recognized by experts, and the Course, devised according to the educational needs to be satisfied, is then devised, implemented and delivered. However, the educational needs within a continuous education paradigm are subject to a rapid development and change. Therefore, educational needs have to be periodically detected and updated in order to be included within an efficient and timely lifelong learning framework. The Continuous Learning System of Marche Region aims at becoming one comprehensive system able to support the lifelong learning cycle of life. The MarchE-Learning Project, which belongs to the set initiatives promoted by Marche Region, is firstly aimed at developing a permanent framework for detecting the educational requirements of employees working for the Italian Public Administration, especially on e-government matters. Within such a framework, the first step of the Program is aimed at training some employees within the Public Administration itself to be able to detect the needs of their Organization. The first objective of the Project is to teach a representative group of public administration employees the ways in which the analysis of the requirements is to be carried out, examining best practices, tools and methodologies.The goal of this step is to make people in the group able to autonomously detect the educational needs within their own organizations. By making empolyees within Organizations able to detect autonomously the education needs of the Organization, the Project aims at reaching a twofold benefit: on one side, to increase learners' engagement, awareness and control over their own learning needs; on the other, to increase the effectiveness, efficiency and timeliness of the recognition of the educational needs themselves.

The second step of the MarchE-Learning Project is aimed at offering courses concerning egovernment issues in blended mode. This second step can be contextualized within the elearning activities for Public Administration carried out according to CNIPA guidelines [START_REF] Cnipa | Linee guida per i progetti formativi in modalità eLearning nelle PA, I Quaderni[END_REF]. Province level. To understand Italian administration system we have to clarify that a Region is the largest local administration unit, and it may include a certain number of provinces, which, in turn, are composed of many municipalities. Mountain communities are composed units including more municipalities, usually within the same province. At present the government of Marche Region is working to include in its network all administration units, even the smallest and most difficult to reach, combining its structure with the normal telephone network and local WiFi networks. So far, the regional government has used a proprietary Electronic Learning Environment, but this program is planning to start using an Open Source Learning Management System, in line with suggestions from the central government.

The other main aspect of the project is the methodological and organizational framework, which will be discussed in this paper.

Program Framework

Marche Region covers an area in Central Italy between the Adriatic Sea coast on the East and the Apennines Mountain range on the West. Within this area there are major differences between coastal areas, which enjoy fairly developed communication structures and facilities, and mountain areas, where people often live in small villages which are difficult to reach, because of lack of good roads and ICT infrastructures. The landscape also includes a series of parallel valleys reaching up towards the mountains from the coast. In such a situation it was very important to pay special attention to disadvantaged areas, in order to reduce differences in service availability and promote social inclusion. The mid-term goals of the program are:

• the set up of a WEB based Environment • the automation of work processes within the Regional Training School, including monitoring and evaluation activities • the creation of a network of reference workers, whose training is currently being planned and will be shortly implemented • the definition of a methodology and survey tools as basis for a permanent process of detection and analysis of training needs. In order to fulfil the above objectives the work has been organized into 8 work packages and assigned to three different work groups. One group is working on technological infrastructures, the second is working on the internal processes system while the third is taking care of the network of reference workers. This last part of the program involves political and institutional aspects, since it includes the establishment of contacts in public institutions, the communication of the program purposes, the selection of offices and workers.

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The first step of the activity is the selection of a group of workers inside offices in disadvantaged areas: they shall be employees who are in a position to understand the training needs in their institution, besides being motivated to acquire new skills, some of which related to information and communication technologies. The selection shall be preceded by two preliminary activities:

• an inquiry on the organizational structure of mountain communities aimed at defining functions and competences already existing within the organizations, assessing the present situation of personnel training on samples that may be representative of the whole region, gathering data for the definition of a draft map of possible training needs and a model for continuous detection of such needs, choosing the reference personnel within those organizations assessed during the inquiry as well as preparing a questionnaire for employees in order to obtain a more accurate picture of the identified competences and functional areas • the definition of a set of qualifying indicators for the profile of the reference worker, to be used as a tool to identify the most suitable people for such role. The goals of the first step will lead participants to be able to:

1) acquire basic knowledge about the learning processes;

2) define goals and objectives of a learning program according to the educational needs which have to be satisfied;

3) know the models underlying educational needs detection; 4) select an appropriate model in order to detect educational needs in the context in which everyone operates; 5) interpret the results given by a model for educational needs detection.

The inquiry shall be carried out through interviews with managers of the involved mountain community offices. Interviews shall be focused on the state of art of training, need detection and training offer within mountain communities in the present situation. Information obtained through the interviews shall be used to prepare a questionnaire for training need detection within the Public Administration of Marche Region. The outline of the interviews includes a first section with questions on services offered to the public and ways to access such services, on communication and co-operation with other local administration entities, on already existing ICT infrastructures. A second section of the interview is about training need detection, while the last section concerns the present situation of training offer, including questions on the modes of delivery, the production of course contents, the availability of infrastructure for on-line training and its characteristics and accessibility, the monitoring and evaluation activities, if any are carried out after training delivery.

The work group has already defined the indicators and pre-requisites for the selection of reference workers.

• They must be medium-high level employees, who have a clear vision of their institution's mission The selected workers shall be trained through a blended mode course to apply methodologies for detection of training needs within their institutions and to apply a set of analysis procedures. The e-learning and distance learning element of the course shall be seen as a model to design and deliver training activities, since such methodology is specifically suitable for an area of intervention where users come from different organizational environments and from geographically different situations. In fact the workers who shall act as need detectors and who will be the first users of the training structure come from institutions that are different in size and within their organizations they may have very different roles. These workers shall develop into a new professional profile, specialized in detecting and analyzing training needs by using the most effective tools.

•
Thus distance learning represents the most appropriate methodological and organizational choice in order to fulfill the following short term objectives: Administration for their dissemination to all concerned personnel (Knowledge Management). The pedagogical approach of the courses has to be the socio-constructivist, problem-oriented and active paradigm, while a strong focus shall be placed on the social aspects of learning, fostering the development of a Community of Practices, which, according to Wenger [START_REF] Wenger | Communities of practice: learning, meaning and identity[END_REF] is a learning organism with three main aspects:

•
• Reciprocal commitment, understood as participation, involvement and responsibility towards the community and its activities • A shared repository of resources, meant to be the arrival point of common activity • A common enterprise, in the sense of negotiated sharing of objectives and strategies. Participation produces sense of belonging, and this should be very appropriate in the process of organizing a network of people to carry out a specific function.

According to the principles of project management, the activities for the on-going phase of the program have been distributed among the work groups according to eight packages, as listed below:

Overall Program Management

It includes strategic planning, implementation control and result evaluation, and it is carried out by the team of Università Politecnica delle Marche.

Coordinating activities and communication

This work package is entrusted to the Regional Public Administration Training School and includes the monitoring of activities, the choice of specific didactic methodologies for the selected target, the presentation of the program and its methodologies to the reference workers and all involved institutions, the organization of workshops with course target to detect specific needs and finally the evaluation of the whole program

Analysis and strategies

The university team is responsible for this work package which includes the selection of a list of reference workers inside those institutions reached by the service and the analysis to define procedures for detection of users' training needs. All relevant data shall be gathered on-line through questionnaires and/or interviews and then processed. This work package also includes the design of procedures which shall be the base for future services, with specific focus on instruments and methodologies for the transition to the new integrated training system. Within this package is also included the definition of questionnaires and templates for interviews. Finally, detailed and overall analysis of the results of the learning process, as well as interpretative reports and proposal of guidelines for program future activities are part of this unit of activity.

Basic technological infrastructures

The university team shall also be responsible for the study of accessibility and usability of the training system, the implementation of dynamic CMS modules for information communication (News, Courses, Schedules,…), the implementation of User Management modules for dynamic management of users and their training profiles, the implementation of modules for communication with the Data Base system that may accept new services through a homogeneous protocol, the starting population of the Data Base with users' forms and profiles as well as knowledge relational networks.

E-learning services structure

This work package, in charge of the University team, includes the implementation of LMS modules to integrate in the platform the functions of Forum, Glossary, Library, FAQ, Virtual class with support for tutoring and social learning, News, Reports about student profiling and course profiling. It includes the implementation of modules to process feedback data from LMS and to create automatic reports, as well as the implementation of modules for creation and on-line delivery of questionnaires and interviews, with automatic data gathering and aggregation.

Content production for e-learning course

The university team shall look after the planning of didactic strategies and delivery modes of the courses, and consequently elaborate contents for the course aimed at the network of reference workers and produce the relevant Learning Objects to publish in e-learning mode.

System set-up

This part of the program shall be carried out by the Technical Team of Marche Region Government, and includes the set-up of a server unit suitable for the specific working requirements of the selected software and the work load required by the number of users of the system; it also includes the installation of all necessary software applications as well as the testing of all on-line and off-line activities.

Training services delivery, monitoring and evaluation

The university team shall finally be responsible for service delivery, from assignment of passwords and distribution of training material for the use of e-learning services, to course delivery and platform maintenance as well as gathering and analysis of all relevant data for monitoring and evaluation.

Learning Activities

The first step of the Program includes four modules on basilar contents on educational needs detection followed by practical online activities. According to the pedagogical inspiring theories, that is, active and collaborative learning theories, the practical activities take the most part of learning activities.

The first module is devoted to support and promote learners' awareness on the importance of education and educational needs. The participants are required to experience directly the importance of such awareness by carrying out a focus group, followed by a questionnaire on their own educational needs. Moreover, because of the importance of group dynamics for the success of collaborative learning, a part of the module will be dedicated to develop groups interactions.

The second module focuses on the basics of the learning processes; it will introduce the definition of Learning Organization, the cycle of life of learning projects, the importance of competences development, the models for educational needs detection, and surveys different models of education within Organizations. A special attention will be paid to the construction of a shared and appropriate lexicon.

The third module will deal with an in depth investigation on models for educational needs detection. It will focus on the steps of empirical work for educational needs detection.

Moreover, the learning environment and its features will be explicitly introduced. The focus will be explicitly put on collaborative learning tools made available by the environment, as well as on collaborative learning processes. Furthermore, the tutors will be introduced and the role of tutors will be explained. The arrangement of the personal profile of each participant will end the module.

The work will continue online by means of forums discussion on the materials given in the in presence meeting.

The fourth module will discuss on the work done by participants in oline mode and survey some real world case studies on educational needs detection, in the light of what participants have learned in the previous modules. A special attention will be paid to the analysis of the relationships of these case studies with the whole cycle of life of educational project in which they have been carried out. Participants will be required to continue online the work and to trasfer what they have learned to the needs of their own Organization. As a result, participants shall discuss the choices regarding the procedure, the methods, the paths and the channels each of them has followed.

The fifth module will be devoted to a discussion on positive and negative points of each approach. Participants are required to express their doubts, if any, and to discuss collaboratively on advantages and drawbacks of the different approaches, with particular regard for the structure and needs of every single Organization.

As an overall result, each participant will realize a project work in which he/she chooses an approach and develops the educational needs detection accordingly, and will discuss collaboratively the strengths and the critical points of the approach.

Further activities, devised within the second step, will concern the delivery of a Coursedevised according to CNIPA guidelines [START_REF] Cnipa | Linee guida per i progetti formativi in modalità eLearning nelle PA, I Quaderni[END_REF] -focused on e-government. The second step will take carefully into account the educational needs detected during the first step, and will offer a chance to satisfy them.

Monitoring and evaluation

The aim of monitoring and evaluation of an on-line course is to improve the quality of training and to check the acquisition of the skills targeted by the training plan. Monitoring takes place while training is going on for the purpose of controlling effectiveness and correcting any possible malfunctioning. Monitoring should guarantee that any possible shortcoming detected in service quality receives suitable corrective actions, since quality is represented by those characteristics which enable the service to satisfy expectations and training needs, in line with the users' learning goals identified in the training plan.

There are many different aspects to be monitored and evaluated and each of them is aimed at detecting specific shortcomings or malfunctioning, as well as positive aspects, of the training process. The main aspects to be monitored concern: the user, the process, the product. Monitoring and evaluation are complex processes composed of different phases, each of which has its own objectives, methodologies, tools and schedules. Data gathered through monitoring and evaluation are then inserted in a data base to testify individual training itineraries and overall system performance.

In our program, the monitoring and evaluation framework has been designed upon the theoretical bases of Kirkpatrick's four levels of evaluation [START_REF] Kirkpatrick | Evaluating Training Programs. The Four Levels[END_REF], combined with the practical checklists provided in CNIPA's guidelines for e-learning training activities for Public Administration [START_REF] Cnipa | Linee guida per i progetti formativi in modalità eLearning nelle PA, I Quaderni[END_REF] and in the methodological guide for quality analysis in distance training over the Internet, produced by Funcación Universidad-Empresa de Valencia [START_REF] Vv | Methodological Guide to the Analysis of Quality in Open and Distance Learning delivered through the Internet[END_REF] as the result of a European Union financed project.

In the project's simplified framework, a first phase of monitoring takes place before the beginning of the course, and concerns the knowledge and skills the user already has, as well as his/her motivation and expectations; it is carried out using questionnaires or focus groups and it allows the course manager to prepare recovery actions that may be ready when the learning activity is already under way, or even activate a personalized learning itinerary. Then we have a phase of process monitoring, in which the learner's participation is measured through analysis of the platform automatic tracing system, in order to understand the development of his/her interaction skills; in this same phase it is also possible to verify the service quality, checking the efficiency of the back office of the program. Moreover, the overall efficacy of the course has to be carefully checked during delivery: monitoring of the learning process is carried out through self-evaluation tests or formative tests, as well as looking at the online behaviors of the learners. Other phases are about product monitoring, to check the effectiveness and usability of learning objects, and learner monitoring, to verify the social atmosphere in the user group, the level of satisfaction, the perceived utility and effectiveness of the course in terms of organization, didactic strategy, resources and technology, through analyzing various kinds of educational data, such as log data, text data and exercises done by the learners, as well as questionnaires [START_REF]International Working Group on Educational Data Mining Activity[END_REF]. The next phase shifts from monitoring to final evaluation, to measure the level of success in the learning process.

According to Kirkpatrick [START_REF] Kirkpatrick | Evaluating Training Programs. The Four Levels[END_REF], it is possible to distinguish four criteria on which we can base the evaluation of an e-learning program:

• Reaction, that is how the learner felt about the training experience • Learning, that is the measurement of the increase in knowledge and skills after the program • Behavior, that is how much the learner's behavior improved or changed after the training activity • Results, that is the effect the training activity had on the organization environment. Of course there are different times and instruments to carry out all of these forms of evaluation. Actually, in most cases evaluation is limited to the user's reaction, verified through a questionnaire, and the user's learning, controlled through final tests, since some time has to pass before changes can be detected in the learner's behavior and even longer has to pass before the institution can realize the impact of the new knowledge acquired by the employee on work organization and effectiveness. In the MarchE-Learning program the idea is to follow the indications provided by the report "Designing and Evaluating training, a guide for Public Administrations" [START_REF] Mipa | Programmare e valutare la formazione, una guida per le amministrazioni pubbliche[END_REF], that is to carry out all four types of evaluation, so that resources used in training can be used in the most effective way, to bring a further contribution to the growth of service quality.

Expected results

As it may be evident from the double source of its funding, the MarchE-Learning program has two different levels of expected results. On one side, thanks to the activities supported by the Inter-Ministry Committee for Economic Planning, there is a first shorter term result of a network of specially trained workers who will be able to identify training needs within their Public Administration units in disadvantaged areas. On the other side, CNIPA's support has a simpler short term result of a basic infrastructure for communication and on-line distance learning that is going to be used for the up-coming training course for reference workers, but CNIPA's intervention and guidance has a much further reaching expected result.

The reference network for training need detection and the technological communication infrastructure are set up with the aim of developing into an integrated training system for the whole of Public Administration entities of Marche Region. A long term result would then be an e-learning portal connected to a training need detection network in order to offer all the personnel of Public Administration entities of Marche Region easy access to a set of relevant resources for training and updating of their personal and professional skills. The fields and sectors in which training activities will be carried out may include work procedures, law and administration, human resource management, organization management, information and communication technologies, languages and any other field which may be identified by the need detection network.

The e-learning environment will be part of an integrated training system which will be prepared to carry out most activities connected with training and on-line training: need detection, planning, production and organization of learning activities, course delivery, tutoring, monitoring and evaluation, readjustment. Furthermore the system should be an important instrument to let the culture of LifeLong Learning grow among Public Administration personnel, in order to foster individual self-realization, professional updating and social inclusion.

Obviously, such an ambitious program has to be developed step by step, and in this paper we have only discussed what has already been defined.

Conclusions

It is difficult to draw conclusions from an activity that is still in progress, but what can be stressed about this experience is the strategic value of co-operation between Public Administration and Academic Institutions in order to offer the former the most updated knowledge on state of the art of Information Technologies. Especially in Latin countries like Italy, such projects are required to overcome traditional problems of governmental offices, which tend to be slow and become attached to their procedures rather than be open to changes and improvements.

On the other hand, the most innovative aspect of the program is the creation within the Public Administration of a new professional profile: a person who masters some methodologies to detect and understand the training needs within his/her organization and can be the reference point for the elaboration and continuous adaptation of an institutional training plan. The Marche-Learning project aims at properly addressing problems connected with continuous detection of training needs.

Eventually, the program is trying to offer a setting to verify the fact that e-learning, in spite of initial heavy investments, once it becomes an established procedure, can both save resources and offer ways to make training more effective and available to employees who otherwise would not be reached because of problems with transport, communication structures or work schedules.

  The development of a framework program to set up a Continuous Learning System for Public Administration personnel of Marche Region is a local example of the effort to reach the proposed level of e-learning training and to apply CNIPA's guidelines. Actually the goal of delivering one third of training through e-learning is still far from being reached. Most Training Offices within the Public Administration structure have made investments to set up basic IT tools, mainly for organizational and management aspects of training, aimed at improving the planning of training activities and at speeding up routine procedures. . A basic aspect of the development of the system is the completion of the technological communication infrastructure. The government of Marche Region already has a fairly strong broadband network structure connecting all local Public Administration offices, down to

  The MarchE-Learning program has the long term goal of creating a service structure for human and technological resources at the level of Marche Region, introducing e-learning in local administration institutions. The action is being carried out thanks to a double financing action: on one side CIPE (Inter-Ministry Committee for Economic Planning) provided funds to sustain Public Administration training in disadvantaged areas, on the other CNIPA offered economic support to create the technological infrastructure to host e-learning training for the personnel of Marche Region. The part of the activity aimed at sustaining disadvantaged areas involves the selection of workers in mountain site offices who shall be trained to become part of a network for the detection of training needs, thus contributing to the planning of future training activities. At the moment there are 15 administrative entities involved in the program: 13 mountain communities and two municipal administrations.

  They must have been in the Institution for at least two years. • Their role within the institution can be different, but hopefully they should have experience in different areas of administration, most desirable areas being training, information systems, human resource management. • They must have organizational and/or training competences. • They must know e-Government procedures and services, as defined in the above mentioned CNIPA guidelines • They should have already acquired experience in the implementation of e-Government policies and/or in the planning of functional innovation activities within Public Administration services. • They must be experienced in team work • They should have personal experience of training courses delivered at least partly on-line • They should have some experience in projects in association with other institutions within the Public Administration.

  Creation of a training offer which is relevant to present needs at different levels among the personnel of the Public Administration of Marche Region • Creation of a technological communication infrastructure which in the future shall permanently support exchange and circulation of knowledge and experiences within the local Public Administration • Valorization of knowledge and experiences developed in the local Public

1 .

 1 Overall Program Management 2. Coordinating activities and communication 7(13) 3. Analysis and strategies 4. Basic technological infrastructures 5. E-learning services structure 6. Content production for e-learning course 7. System set-up 8. Training services delivery, monitoring and evaluation.

September 26 -28, 2007 Villach, Austria11(13)

Authors:

Silvia Rita, Viola, Dr. DIIGA, Universita '


Sabine Moebs

Christophe Piombo

Hadj Batatia

Stephan Weibelzahl
email: sweibelzahl@ncirl.ie

Stephan

Dr. Weibelzahl

A Tool Set Combining Learning Styles Prediction, a Blended Learning Methodology and Facilitator Guidebooks -Towards a Best Mix in Blended Learning

Keywords: Blended Learning, Felder, Learning Styles, ILS, Probability Estimation, Metadata, Facilitator

One of the challenges in the development in blended learning is to facilitate the individual learning styles of the learners. The alignment of a learning styles assessment with a learning methodology, a mapping between learning styles and social media, recommendations in a guidebook for facilitators and a checklist provide a tool set for a sustainable approach for a responsive learning environment. This paper analyzes how the different approaches, methods and studies interact to form an overall tool set to develop a learnercentered mix in blended learning. It proposes a tool set to adapt blended learning to the learning styles of the learners.

Introduction

Estimating user characteristics is essential for many applications that require adaptation [START_REF] Brusilovsky | Preface to Special Issue on User Modeling for Web Information Retrieval[END_REF]. To facilitate the individual learning styles of the learners the estimation of the differences in students' learning styles, approaches to learning and intellectual development levels [START_REF] Felder | Understanding Student Differences[END_REF] is crucial. The underlying principle consists of estimating the needs of the user in order to adapt the content. In student modelling, Bayesian networks are used to estimate students' abilities at different levels of granularity. The underlying probabilistic model allows handling uncertainty in the assessment of students' knowledge and understanding [START_REF] Conati | Using Bayesian Networks to Manage Uncertainty in Student Modeling[END_REF], or users' plans and goals [START_REF] Carberry | Techniques for Plan Recognition[END_REF]. Küper and Kobsa [START_REF] Küpper | Tailoring the Presentation of Plans to Users' Knowledge and Capabilites[END_REF] presented a plan generation approach for achieving the user's goals consedering their capabilities. This paper proposes a set of tools and methods to adapt blended learning scenarios to the learning styles of learners. The tool set is made of four main components presented in the following sections. The first component describes an original probabilistic method to automatically estimate the learning style of a student based on Felder's index of learning style [START_REF] Felder | Applications, Reliability and Validity of the Index of Learning Styles[END_REF]. The second component presents a pedagogical methodology that structure activities taking into consideration domain tasks, blended learning activities, pedagogical objectives, and learning style requirements. Component 3 presents a Guidebook for tutors providing suitable scaffolding for different learning styles as well as online and face-to-face learning. Component 4 shows a customizable checklist for blended learning courses as results of a rankingtype Delphi study.

Estimation of Learning Styles Based on a Bayesian Network

The learning style is a cognitive characteristic of individuals that depends on the way they perceive and process mentally information. Often the style is represented using multiple dimensions estimated statistically. A review can be found in [START_REF] Riding | Cognitive Styles and Learning Strategies[END_REF]. Every model suggests an instrument to measure the individual's style. They all share the same method: estimate the style, which is hidden information, from observable data representing the user behaviour. This section presents a Bayesian network to estimate the learning style of a student from observable data characterizing the blended learning material and the student's performance. We first present the structure of the network. Second, we describe the way we use this network to progressively estimate the style and reinforce the tables of probabilities.

The proposed Bayesian network is based on the Index Learning Style (ILS) established by Felder and Solomon [START_REF] Felder | Understanding Student Differences[END_REF]. Two types of nodes are used: i) observable variables that describe the learning task, the learning material, and the user behaviour; and ii) non-observable variables that represent the dimensions of the learning style. The dependencies between these variables are based on pedagogical assumptions. The pertinence of a learning material to achieve a given learning objective depends on the student's learning style. In other terms, the learning objective and the learning style have a causal influence on the choice of the learning material. The second assumption consists in considering that the student's learning behaviour and performance depend on the matching between the task, the learning material and the student's style. This means that the task, the learning material and the style together have a causal influence on the performance. Each of these observable variables describes different aspects [START_REF] Piombo | Réseau bayésien pour la modélisation de la dépendance entre complexité de la tâche, style d'apprentissage et approche pédagogique[END_REF] like the media used in the material, the type of pedagogical activity, the type of exercises, the learning objective, the performance, and the nature of the knowledge. In order to describe blended activities, we use a set of variables including collaboration, discussion, and tutoring. The ILS considers that people's approaches to perceive and mentally process information can be classified according to four dimensions. This theory assumes that a student's learning style can be estimated by a belief measurement projected on four dimensions [START_REF] Felder | Understanding Student Differences[END_REF]. The first dimension represents the sensorial dimension. It measures the way (visual or verbal) the student prefer to perceive information (e.g. images, graphics, text or sound). The second dimension represents the progression dimension. It defines the way (global or sequential) the person prefers to progress in a learning task (e.g. linear constrained navigation or selective navigation). The third dimension represents the thinking dimension. It varies from reflective to active (e.g. practice, group work, experimentation, personal work, demonstrations). Finally, the fourth represents the reasoning dimension: ranging from intuitive to sensing (e.g. to progress from theory to practice or from examples to theory). These two last dimensions have a causal influence on the choice of the blended activities.

An initial set of data gathered from an empirical study [START_REF] Batatia | Probablistic estimation of learning styles from observable data[END_REF] was used to calculate the conditional probabilities for each node. The resulting network is instantiated for every new student. The learning experiences of the different students are periodically used to reinforce the tables of probabilities of the network.

For a new student, we assign the most probable in the student's population i.e. style variables are set with the values corresponding to the highest probabilities in the initial network. This will be refined after the first learning activity. For subsequent utilisations of the system, style variables are set to the student's style estimated during

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the last activity. A direct inference is, thereafter, performed in the network to calculate the conditional probabilities of the material variables. The values corresponding to the highest probabilities are selected. These metadata are used to compose a learning activity that is proposed to the student. Monitoring the student's behaviour during the learning task leads to measuring their performance. This is set as evidence in the network and a backward inference is performed to refine the student's style. After a certain number of iterations, the network converges to a stable estimation of the learning style.

Blended pedagogical design methodology

In the context of this tool set, we define the domain model as a set of dependent pedagogical artefacts that include domain tasks, domain skills, learning objectives, learning activities, and metadata of learning material. This model is represented in the form of an ontology. This is made of three layers: domain, pedagogy and content structure. To build such a model, we established a methodology that composes of few phases ranging from needs analysis to content packaging. Each phase creates or enriches the ontology. The domain layer contains a hierarchical structure of tasks; and for each task, the required skills. In addition to classical pedagogical learning activities such as information presentation, practice, and exercises, the pedagogical layer contains other types of activities such as project based learning, collaboration, discussion, tutoring. These activities can be online or face-to-face, individual or group work, synchronous or asynchronous. They might require live sessions or offlinedeveloped material, need assistance from instructor, coach or peers and might require tools.

As part of the UP2UML European project, a metadata schema has been created in order to store this information in the ontology. Some of these metadata are used during content creation (title, scenario, description…); the others, such as the nature (blended, self-paced), delivery mode (online, face-to-face), synchronization (synchronous, asynchronous), actors (group, individual, system, instructor, tutor, coach, organization, peers, class), narrative (live, offline) control the delivery mode. These metadata serve also as observable variables (or nodes) in the Bayesian network. They are used to adapt the activity to the learning style of the learner. In order to assist the training designer, we created reusable patterns of blended activities. Three main patterns have been specified: -Course pattern: made of activities related to organization, planning, meetings, exams… -Project pattern: made of activities related to setting up, running and assessing project activities -Practice pattern: made of activities related to setting up, running, and helping with practical (or laboratory) activities Each of these patterns is structured as three sequences of activities:

-start: a sequence of activities to start a series of other activities that have relationships -execute: a sequence that allows completing the series of activities -terminate: a sequence that ends the series of activities Figure 1 shows the course pattern example.

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Figure 1: course blended activities pattern

The resulting ontology represents a rich body of information and knowledge related to teaching the concerned domain. It is used in different ways. The first utilisation is to guide the creation and structuring of the content material for teaching. The second utilisation is to support an adaptive system to tailor pedagogy to learning styles. Third, parsing tools allow to extract a guidebook that can be used by facilitators in blended learning courses.

Guidebook for Tutors

Guidebooks are a formalized way of describing blended activities. They provide instructions for course facilitators or tutors on how to conduct a course using the available course material. Guidebooks may contain different syllabi based on the same material. In particular the proportion of self-paced and blended activities may vary as well as the pedagogical approach [START_REF] Moebs | Supporting Facilitators of Blended Learning with Guidebooks[END_REF]. It assists facilitators in providing suitable scaffolding for different learning styles as well as online and face-to-face learning. Its main function is to outline the options for face-to-face activities under special consideration of different learning styles of the learners. By prescribing the setting and possible usage of the material, guidebooks aim to guarantee a constant standard for the course across facilitators. Interviews with trainers and providers of blended learning in ongoing research provide a diverse picture. The provider group went so far to actually use the guidebook to instruct the tutors in detail which materials to use when and how to conduct the sessions. Trainers on the other hand pointed out that a guidebook would take responsibility from the trainers. Both groups agree though that a guidebook can help to provide constant quality for the delivery of the face-to-face sessions. The following guidebook structure was developed and has been used for the test of the course as part of the UP2UML project. It has two main parts, the structure itself and the information for the tutors. The structure reflects the pedagogical design methodology, dividing the course into the course pattern as outlined above. The "course start" as well as the "course execute" list the standard activities for this part of the course. The "course execute" part branches out to self-paced learning and blended activities and from there into detailed activities. The information part provides the three main types of information required by the tutors and respectively the learners regarding practice, projects and assessments and which information is required in regard to these activities. The student's learning style is constantly assessed through the monitoring of the activities. Therefore it can also be applied for the selection of self-paced or blended activities and the tools most suitable for the student. According to a recent Delphi study the mix of methods and media is an important aspect of blended learning is [START_REF] Moebs | Blended Learning: Towards a Mix for SMEs -Stakeholders and their Priorities[END_REF]. To accommodate the needs of today's learners a mapping of social media tools and learning styles [START_REF] Baird | Neomillenial User Experience Design Strategies: Utilizing Social Networking Media to Support "Always On" Learning Styles[END_REF] has to be considered. Baird & Fisher outline in a recent publication a mapping between social tools for Gardner's multiple intelligences [START_REF] Gardner | Frames of the Mind[END_REF]. These can be applied to the guidebook structure and thus improve the quality of the learning for different learners. A similar approach for other learning style concepts can easily be adapted.

A Checklist for Blended Learning

A three-round ranking-type Delphi study following the study design described in [START_REF] Schmidt | Managing Delphi Surveys using nonparametric statistical techniques[END_REF] with stakeholders of blended learning [START_REF] Moebs | Blended Learning: Towards a Mix for SMEs -Stakeholders and their Priorities[END_REF] gives an indication what different stakeholders consider important for successful blended learning. The study asked what works best in blended learning and included sub-panels of learners from SMEs (small and medium-sized enterprises) from the IT and the tourism industry, a control group with learners from large companies as well as trainer and providers. Both SME sub-panels selected the following aspects of blended learning into the final "Top 10". The learners from IT SMEs also selected these aspects:

• Workplace-related learning • Feasibility • Geographical independence • Individuality Detailed descriptions of the aspects can be found in [START_REF] Moebs | Blended Learning: Towards a Mix for SMEs -Stakeholders and their Priorities[END_REF]. Keeping in mind that a Delphi study will give an indication rather than a representative result, the list outlines the selection of a panel of experts for blended learning in SMEs nevertheless and can serve as a quick method to check the fit for a specific learner group.

Figure 2 :

 2 Figure 2: Guidebook structure and tutor information

Figure 3 :

 3 Figure 3: Guidebook structure example

•

  Cost efficiency • Accessibility • Self-paced learning • Efficiency • Time flexibility • Results measurementThe learners from IT SMEs also selected these aspects:• Support mechanisms (IT) • Content design (IT) • Learner-centred (IT) • Quality (IT)

Acknowledgements

The work described in this paper is part of the Up2UML project [Project No. DE-05-B-F-PP-146369] and is partially funded by the Leonardo da Vinci Agency / European Commission.

Author(s):


Chhayarith Heng Uy
email: chhayarith.heng-uy@univ-grenoble-alpes.fr

Carolynn Bernier
email: carolynn.bernier@cea.fr

Sylvie Charbonnier
email: sylvie.charbonnier@univ-grenoble-alpes.fr

Energy Efficient Channel State Classification for Lifetime Enhancement of LPWA Networks

Current Low Power Wide Area Network (LPWAN) wireless transceivers are designed, or configured at deployment time, to function assuming a worse-case application scenario. Most of the time, they waste a significant amount of energy when operated under favourable channel conditions. Energy efficient and accurate channel state classification is imperative for selecting the optimum trade-off between transceiver performance and amount of saved energy, without impacting transmission quality. This work presents a novel, low complexity channel state indicator and a simple mono-feature classifier for channel state recognition. The classifier is trained using a set of experimental data acquisitions and has a 96% accuracy when tested with a new collected data set. Specially designed for LPWA applications, the classifier is capable of distinguishing undisturbed channels from those suffering from both mobility-induced fading and interference, while operating at low to medium SNR.

I. INTRODUCTION

The ultimate goal in the field of the Internet of Things (IoT), and, more specifically, of Low Power Wide Area (LPWA) wireless networks, is the development of strategies to reduce the average energy consumption of the autonomous network nodes. To this end, the automatic adaptation of the transceiver performance to the varying Radio Frequency (RF) channel is a research perspective whose aim is to eliminate the unnecessary energy consumption in moments when the signal to noise ratio (SNR) is in excess with respect to the communication link budget. Indeed, a lot of recent work has focused on the many tuning knobs that can be used to trade excessive SNR against energy consumption, e.g. output power, modulation order, coding rate, frame length, receiver noise figure, ... However, in all cases, to enable any channel state adaptation algorithm, it is necessary to develop energy efficient channel state recognition mechanisms.

Wireless sensor nodes using LPWA communication technologies represent a large fraction of IoT networks. These nodes are typically deployed for low mobility, km-range applications. The large distances involved imply that the RF communication channel will have a wide dynamic range, making it necessary, and particularly for systems employing ISM bands, to identify at least three channel states:

• An Undisturbed state where noise can be modelled as a white Gaussian random process;

• An Interference state in which simultaneous RF transmissions can impede the desired transmissions; • A Fading state in which the transmission quality is impacted by the movement of the node itself or of humans or objects in the node's environment.

The identification of these three channel states, versus 2-state approaches which are commonly proposed in the literature, makes it indeed much easier to develop adaptation strategies that are specific to the observed propagation conditions.

Due to the large distances, the main energy cost for LPWA nodes is the one required for wireless transmissions. Any channel state identification mechanism must therefore avoid the transmission of dedicated frames or symbols or, in case of erroneous channel classification, of repeated frames. In addition, the calculation complexity of the channel state indicator (CSI) must be low to avoid the power burden on the wireless node's application processor. If we wish to ease the development of new MAC or network-level adaptation algorithms, the CSI must be clear and simple to use, with ideally a single value computed per frame. And finally, since the future channel-aware system is meant to function at low to medium SNR, the CSI must perform at its best in these situations.

This paper focuses on the development of low complexity channel analysis signals (CAS) which provide relevant features for accurate channel state classification. While this present work is limited to the LECIM (Low Energy Critical Infrastructure Monitoring) FSK PHY of the IEEE 802.15.4-2015 standard, our aim is to show that such an approach can be generalized to other LPWA standards such as LoRa, and including cellular standards such as NB-IoT. With this demonstration, our hope is to encourage the development of future transceivers that natively embed such a capability in order to enable the massive deployment of channel-aware protocols.

The contributions of this paper are:

• To the authors' knowledge, the first channel state classifier tailored to the requirements of LPWA networks with a specific focus on the life-time enhancement of energy autonomous nodes; c 2019 IEEE

• A 3-state channel identification algorithm adapted to low to medium SNRs built upon a low complexity and single frame classifier with 96% accuracy. • An experimentation-based classifier design methodology which can be easily generalized to other LPWA communication standards. This paper is organized as follows: the specifications of the desired classifier are given in Section II, followed by related work in Section III. Our design methodology and proposed approach are presented in Section IV and V, respectively. Experimental results are discussed in Section VI.

II. CLASSIFIER REQUIREMENTS

Our aim is to design a channel state indicator dedicated to LPWA networks in which application throughput is typically very small and where the most important requirement is improving node lifetime and not the throughput of the wireless link. For heavily duty-cycled IoT applications in which frame transmissions are relatively 'rare', single-frame channel diagnostics is a must. The orders of magnitude energy cost difference between RF data transmissions and local, on-board data processing implies that the long periods between successive frame transmissions can be leveraged for computations that can be performed at low clock rates. Our goal is therefore to design a simple to use, low complexity, information-rich channel state indicator (CSI) able to ease the development of high performance adaptation protocols. Indeed, the more precisely we can describe the instantaneous channel conditions, the better the adaptive protocol (whether at MAC or routing-level) can make appropriate reconfiguration decisions. The proposed classifier will have the capacity to discriminate between three channel classes: 'undisturbed', 'fading', when the channel is impacted by mobility-induced fading, and 'interference', when simultaneous RF transmissions are detected.

Additionally, the classifier must be accurate in low to medium SNR situations since they are those in which the future channel-aware system is meant to function (in steadystate). Recognizing the channel state at low to medium SNR is imperative to save a maximum of energy without impacting transmission quality. In particular, it is imperative to know the channel state before the onset of symbol errors (due to channel imperfections) if the frame is un-coded. Finally, the classifier must not become 'blind', i.e. incapable of recognizing interference or fading states, at medium SNR levels.

III. RELATED WORK

Practically all previous work on channel quality recognition aimed at either increasing the network throughput or improving the reliability of routing protocols. A huge body of work exists in which authors employ indicators available on Commercial Off The Shelf (COTS) radio transceivers, such as received signal strength indicator (RSSI) or link quality indicator (LQI), or MAC-level metrics such as packet reception ratio (PRR), and so on [START_REF] Baccour | Radio link quality estimation in wireless sensor networks: A survey[END_REF]. However, many authors have discussed the limited accuracy and potentially misleading nature of such metrics [START_REF] Baccour | Radio link quality estimation in wireless sensor networks: A survey[END_REF]- [START_REF] Reis | Measurement-based models of delivery and interference in static wireless networks[END_REF]. More generally, using these metrics, it is often difficult to understand the reasons behind the loss of channel quality. And metrics requiring many transmitted frames (such as PRR) necessarily miss short-term variations of channel quality.

The inefficiencies of these methods have motivated recent work on finer-grain approaches able to discriminate between different types of channel impairments [START_REF] Wu | Chip Error Pattern Analysis in IEEE 802.15.4[END_REF], [START_REF] Jamieson | Ppr: Partial packet recovery for wireless networks[END_REF]- [START_REF] Barac | Channel Diagnostics for Wireless Sensor Networks in Harsh Industrial Environments[END_REF]. In [START_REF] Wu | Chip Error Pattern Analysis in IEEE 802.15.4[END_REF], software defined radios programmed as IEEE 802.15.4 [START_REF]IEEE Standard for Low Rate Wireless Networks-IEEE Std[END_REF] receivers with hard chip decisions were instrumented to calculate the per-symbol Hamming distance between the received chip sequences and the predefined pseudo-random codes. A pattern matching analysis of this signal is performed for several consecutive frames and the cumulative result feeds a classification algorithm that distinguishes between normal (undisturbed), attenuation (due to the propagation medium, reflection and propagation distance) and interference types of channels.

The work in [START_REF] Wu | Chip Error Pattern Analysis in IEEE 802.15.4[END_REF] is a special case of a more general approach that was presented earlier in [START_REF] Jamieson | Ppr: Partial packet recovery for wireless networks[END_REF] and [START_REF] Vutukuru | Cross-layer wireless bit rate adaptation[END_REF]. Here, the higher protocol layers are provided not only with the frame's decoded symbols, but also with information corresponding to the probability that each decoded symbol is correct, termed SoftPHY hints by the authors. Using this information, a persymbol SNR can be estimated precisely. Sudden jumps in SNR can be identified as frame collisions. As discussed above, the SoftPHY hint for a symbol can be simply the Hamming distance separating the decoded and ideal symbols in a hard decision decoding spread spectrum receiver or it could also be the modulus of the log likelihood ratio that is produced by certain linear convolutional or block decoders. However, since all of these SoftPHY hints (one per symbol) are passed to the higher layers without further processing, the authors assume that it is up to the higher layers to do the job of signal analysis and classification.

In [START_REF] Barac | Channel Diagnostics for Wireless Sensor Networks in Harsh Industrial Environments[END_REF], the authors use forward error correction (FEC) on top of (normally un-coded) IEEE 802.15.4 transmissions in order to extract the position of bit errors within each frame. Using this information, the authors compute a symbol error density (SED) defined as c/(S L -S F + 1), where c is the number of detected symbol errors, and S L and S F are respectively the last and first error positions. Experiments in the presence and absence of interference are used to find a discriminating threshold for a binary classifier. While this technique has the important advantage of being applicable to COTS transceivers, the coding overhead results in a significant power consumption penalty. In addition, only two channel states are defined, limiting the possibilities offered to the adaptation protocol. We include a detailed comparison of the SED-based classifier with our work in the results below.

IV. EXPERIMENTATION-BASED METHODOLOGY

An experimentation-based methodology is used to design our classifier in order to ensure that it can correctly handle real-world signals, as opposed to signals which would be constructed only through simulation. While this methodology is applicable to any LPWA wireless communication system, for this work, we choose to implement radio transceivers based on a variant of the IEEE 802.15.4-2015 LECIM FSK PHY standard [START_REF]IEEE Standard for Low Rate Wireless Networks-IEEE Std[END_REF] which is an LPWA standard dedicated to critical infrastructure monitoring. After a description of this standard, the SDR experimentation platform is described.

A. DBB Receiver for IEEE 802.15.4-2015 LECIM FSK PHY

This variant of the LECIM FSK PHY employs the GMSK modulation at 37.5 kchip/s. The frame structure is illustrated in Fig. 2. The preamble is a "00110011" four byte sequence. The 16-bit CRC sequence is based on the generator polynomial G(x) = x 16 + x 15 + x 2 + 1. FEC can optionally be used and is a rate 1/2 convolutional coding with constraint length K = 7. The LECIM FSK PHY optionally uses direct-sequence spread spectrum modulation with a spreading factor from 2 to 16. If FEC encoding is used, only the payload and the CRC are coded, interleaved, whitened and optionnally spread. The header is treated similarly but without the whitening step. The preamble and the SFD are added as is to the beginning of the frame. In the channel state classifier design described below, the use of FEC is not necessary but we have implemented the convolutional coding/decoding step to allow the computation of a SED for performance comparisons. The corresponding DBB receiver is presented in Fig. 1. Blocks in blue handle frame synchronization and start of frame delimiter (SFD) detection. The synchronization algorithm, based on a correlation with the known preamble sequence, also finds the optimal decimation moment, allowing the sample stream to be decimated by a factor of 8. Blocks in yellow perform demodulation while blocks in green compute the channel analysis signal (CAS) which is used to extract the channel state indicator (CSI). The DBB is composed of the following elements:

• FIR1 is a wide band, high order low pass filter with a -3 dB cut-off frequency f F IR1 = 65.7 kHz. The value of this cut-off frequency takes into account the chip-rate, the maximum carrier frequency offset (CFO) of the hardware and a safety margin; • AGC is an automatic gain control block which outputs a controlled amplitude signal. This block simplifies the synchronization algorithm of the DBB by allowing the use of a constant preamble detection threshold; • The CFO block calculates and corrects the carrier frequency offset; • FIR2 is a low pass, high order filter with a -3 dB cut-off frequency f f ir2 = 30.7 kHz. • Next, the Hermitian product between samples A[k] and

A[k -1] is calculated. The Hermitian product is defined as A[k] × A * [k -n]
with n the delay between the two samples. With a delay of 1, the imaginary part of the product is a good approximation of the instantaneous frequency and can therefore be used to demodulate FSK signals.

• Finally, the imaginary part of the signal is the soft input to the de-spreading block. Fig. 3 shows the simulated BER performance of the implemented IEEE 802.15.4-2015 LECIM FSK PHY baseband receiver. The ExpSF4 series are measurements made using the SDR-based platform described below.

B. SDR-Based Experimentation Platform

Since COTS devices do not allow access to DBB algorithms, an SDR platform is needed for experimentation. The goal of the experiments is to provide realistic data for the classifier training set and the test set. Physically, the SDR platform is composed of three universal software radio peripherals (USRP) placed in a 5 m × 4 m room. The first one is used as transmitter (T x), the second as receiver (Rx) and the third as the interferer (Ix). The USRPs are N210 models [START_REF]USRP Hardware Driver and USRP Manual-USRP2 and N2x0 Series[END_REF] from Ettus Research with WBX [40 MHz -2200 MHz] daughter-board [START_REF]USRP Hardware Driver and USRP Manual-Daughterboards[END_REF]. All experiments employ a carrier within the 869.3-869.4 MHz ISM band which is not limited by duty cycle in France, if transmissions are below 10 mW. Since we are interested in studying the behaviour of the receiver in low SNR conditions, attenuators are inserted between each emitting USRP output SMA port (60 dB for T x and 30 dB for Ix) and the half-wave dipole antenna. The interferer Ix frequency carrier has an offset of 100 kHz from that of T x. This allows us to limit the transmission range to approximately two meters which is necessary for leading controlled experiments.

Each USRP is connected to a PC by Gbit Ethernet. Each USRP is driven by GnuRadio and acts as an RF front-end with up/down frequency conversion, analog filtering and DA/AD conversion. For the T x and Ix, complex baseband data generated by MATLAB are fed to the USRP by GnuRadio. For the experiments, 1250 byte length frames were used in order to capture long duration signals which are typical of LPWA frames at maximum coupling loss (2 seconds here). Frames were generated with a spreading factor of 4 and encoded as described previously. For the Rx, the USRP samples the baseband complex sample stream at 300 kHz, i.e. 8 samples per symbol, which is then fed to the MATLAB DBB.

C. Data collection

In order to produce labelled data-sets, three experiments are executed. In the first, frames are acquired in an undisturbed environment. For the second, an interference environment is generated using I x . To avoid wrongly labelled frames, we ensure that all received frames are disturbed by running I x with a duty cycle of 0.4s. We limit our experiments to interferers of inferior frame length with respect to the desired frame since the opposite case can be identified as a sudden change in SNR. The interferer employed in our experiments is an identical IEEE 802.15.4-2015 LECIM FSK PHY transmitter. Only constant envelope interferers are considered (e.g., FSK-based transmissions, LoRa) since these are the most common in LPWA communications. The third experiment is executed in a mobility context, including pedestrian motion near the USRPs, or an arm or object moving trough the direct transmission path between T x and Rx. All signal acquisitions are made at low to medium SNR. The variation of SNR is obtained by changing the T x USRP RF gain. A total of 360 frames are collected,120 for each experiment and labelled 'undisturbed', 'interference' or 'fading'. This is enough for feeding the classifier training and evaluation steps. Only frames with valid CRC were analyzed.

V. LOW COMPLEXITY CHANNEL STATE CLASSIFICATION

In this section, we present the low complexity channel analysis signal (CAS) and channel state indicator (CSI) that were selected to build our channel state classifier.

A. Channel Analysis Signal

Similarly to previous work by [START_REF] Wu | Chip Error Pattern Analysis in IEEE 802.15.4[END_REF], [START_REF] Jamieson | Ppr: Partial packet recovery for wireless networks[END_REF], [START_REF] Vutukuru | Cross-layer wireless bit rate adaptation[END_REF], we directly exploit signals present in the digital baseband receiver to extract a low complexity CAS. The CAS must reflect the certainty level that the received symbol is correct. In GMSK, the symbols (corresponding to chips in the IEEE802.15.4-2015 LECIM FSK PHY) are represented by frequency deviations of the signal. The standard deviation of the absolute value of the instantaneous frequency estimations, already calculated in the DBB thanks to the Hermitian product, is proportional to the uncertainty level of the received chips (and therefore, inversely proportional to SNR). The absolute value of the imaginary part of the Hermitian product has the additional advantage of being robust to fluctuations of receive signal amplitude.

With x k , k = 1, ...N the outputs of the imaginary part extraction block (cf. Fig. 1), a CAS k is computed for each received chip, where

CAS k = 1 win win-1 i=0 |x k-i |. (1)
We define CAS = [CAS 1 , ..., CAS N ] as the channel analysis signal. To compute each CAS k , a moving average over a computation window of win samples is used to smooth the signal and highlight the disturbance footprint. The value win = 1000 is selected which corresponds to a duration of T win = 26.6 ms given the 37.5 kHz sample rate. The choice of win is compatible with the duration of the expected RF interference as well as the expected mobility of objects or humans in the vicinity of the nodes.

Figures 4, 5 and6 show examples of the CAS computed on frames transmitted through channels labelled as 'undisturbed', 'interference' and 'fading'. Observing the curves we can see the temporal footprint corresponding to each channel state. For the 'undisturbed' case, the CAS has a small amplitude and fast variations around the average value, µ CAS . In those labelled as 'interference', the presence of the interferer is clearly visible. In those labelled as 'fading', the duration of the fading phenomena can clearly be put into perspective with the coherence time T c of the channel, i.e. the time during which two transmissions suffer correlated fading effects, defined as 0.4 * c/(vf ), where v is the speed of the moving element, f is the carrier frequency, and c is the speed of light [START_REF] Proakis | Digital Communications[END_REF]. Assuming a sub-GHz carrier, a coherence time on the order of 100 ms is typical of low mobility IoT use-cases. The complexity of the CAS computation is very low, requiring only a modulus computation and a moving average filter in the DBB.

The CAS computation is easily generalized to other standards. E.g. in a LoRa DBB, symbols are recovered as bin locations of an FFT performed on the received sample stream. [START_REF] Talla | LoRa Backscatter: Enabling The Vision of Ubiquitous Connectivity[END_REF]. In this case, the CAS could be defined as the magnitude of the FFT bin corresponding to the LoRa symbol.

B. Low Complexity Channel State Indicator

We propose a very simple and low complexity CSI computed from the CAS defined above which condenses the state channel information into only one value. We define our CSI as the average crossing count (ACC) which captures the variations of the CAS around its average value. An ACC value is computed for each received frame which can be fed to the classification algorithm to identify the current channel state.

To compute ACC, the CAS is centered by its average µ CAS to convert the average crossings to zero crossings. The average Fig. 6. CAS and ACC for two frames transmitted through an 'interference' channel crossing count is the summation of the S k sign switches, described by [START_REF] Reis | Measurement-based models of delivery and interference in static wireless networks[END_REF]. The CSI computational complexity is low and the size of required memory to temporarily save the CAS values depends on the maximum frame length. Fig. 8 shows the labelled ACC distribution according to the different channel states.

S k = sgn(CAS k -µ CAS ) (2) ACC = 1 2 N k=2 | S k -S k-1 | (3)

C. Classifier Definition and Training

In this work, the classification in the three channel classes, 'undisturbed', 'fading' and 'interference', is done using a single feature, ACC. Two thresholds, λ 1 and λ 2 , are required to discriminate between the three classes. The channel state is classified as 'interference' when the ACC is below λ 1 , as 'undisturbed' when ACC is higher than λ 2 and as 'fading' otherwise. The value of λ 1 and λ 2 are set using a learning data set consisting of 180 randomly chosen labelled frames acquired using the SDR platform. Each class, 'undisturbed', 'fading' and 'interference', contains 60 frames. To evaluate the ability of ACC to discriminate between the 'undisturbed' and 'disturbed' (fading and interference) channel states and then between the 'fading' and 'interference' states, ROC curves are built. Let us consider the 'undisturbed'/'disturbed' detector case. A frame with an ACC higher than λ 2 is classified as 'undisturbed', otherwise it is classified as 'disturbed'. The ROC curve displays the true positive rate (the percentage of 'disturbed' frames correctly classified as 'disturbed') as a function of the false positive rate (the percentage of 'undisturbed' frames classified as 'disturbed') for various values of λ 2 , varying from 0 to the maximum value of ACC. Fig. 7 shows the ROC curves for the 'undisturbed'/'disturbed' and 'fading'/'interference' detectors using the learning data set. One can see that ACC proves to be quite efficient in discriminating between 'disturbed' and 'undisturbed' frames as well as between 'interference' and 'fading' frames. The two ROC curves almost reach the performance of the ideal detector i.e. 100% TP with 0% of FP. The two ROC curves can be used to select the value of the two detection thresholds λ 1 and λ 2 . Both are selected from the optimum operating point i.e. the point closest to (0,1), the ideal performance. The optimum operating points provide the λ 1 and λ 2 thresholds of 85 and 530, respectively. Fig. 8 shows the labelled training set ACC distribution with respect to λ 1 and λ 2 . We can observe that these thresholds provide good boundaries between the three classes. Since the ROC curves never pass by the (0,1) point, the classifier is not flawless thus explaining the presence of some error points in the ACC distribution. Finally, the classifier tuned on learning data set is expressed as follows :

if ACC > 530 then the frame is 'undisturbed' else if ACC > 85 then the frame is 'fading' else the frame is 'interference' end if end if

VI. EXPERIMENTAL RESULTS

The performance of the classifier is evaluated on a validation set, made of the 180 labelled frames acquisitions (60 frames per class), which were not used in the training set. The ACC-based classifier is able to correctly classify 96% of the 180 frames, 3 'undisturbed' frames were misclassified as 'fading' and 5 'fading' as 'undisturbed'.

Next, we compare our ACC-based CSI with the symbol error density (SED) indicator presented in [START_REF] Barac | Channel Diagnostics for Wireless Sensor Networks in Harsh Industrial Environments[END_REF]. Recall that this indicator requires forward error correction (FEC) in order to extract the position of bit errors within each frame. The SED is defined as c/(S L -S F + 1), where c is the number of detected symbol errors and S L and S F are respectively the last and first error positions. The authors claim that comparing the SED to a simple threshold is sufficient to distinguish between two channel classes: an interference class and a class where channels suffer from multi-path fading or attenuation (MFA). The authors also claim that multi-path fading and attenuation cannot be distinguished. Clearly, the results shown in Figure 8 show that, using our ACC as a channel state indicator, it is possible to distinguish these two states. To test the authors' first claim and compare the SED with our work, we reuse our acquisition dataset and compute a SED for each received frame. This is possible since the data contained in the acquired frames are encoded using the 1/2 rate convolutional code defined in the LECIM FSK PHY.

First, we observe that the SED is zero when there are no errors in the frame, rendering it unusable in a medium SNR channel. This is a major limitation in a channel-aware LPWA system in which we specifically aim to have wireless links which have low to medium SNR. Since our dataset includes frames received at various SNR levels, we must therefore exclude all of these frames from the analysis. For the remaining frames, we observe that, whatever the channel class, all SED values tend to zero with increasing SNR, simply because errors become less common. Since a fixed threshold is used to distinguish between the two classes, distinguishing these two in the context of variable SNR experiments is thus impossible. SED will therefore be an adequate indicator only in very low SNR scenarios where the decoding algorithm corrects a sufficient number of errors. The channel state indicator proposed in this work does not suffer from this limitation. In conclusion, in an LPWA context, while our approach offers 3-state classification with 96% accuracy, the SED indicator offers only limited use and accuracy.

VII. CONCLUSION

Accurate channel state identification is the cornerstone of lifetime enhancement of massive IoT wireless nodes based on LPWA communication schemes. This work proposes a low complexity channel state indicator computed directly within the DBB and which can easily be generalized to other LPWAN standards. This indicator is used to define a threestate classifier that achieves a correct decision rate of 96% when distinguishing 'undisturbed', 'fading' and 'interference' channels. The proposed indicator remains relevant at medium SNR, when the state-of-the-art SED indicator becomes unusable. Our work will aid in the development of cross-layer channel-aware adaptation strategies. Future work will include experimentation with non constant envelope interferers (e.g. Sigfox) and different propagation environments for the validation of the classifier performance in generalized scenarios.

Fig. 1 .

 1 Fig. 1. IEEE 802.15.4-2015 LECIM FSK PHY digital baseband receiver algorithm with CAS and CSI computation.

Fig. 2 .

 2 Fig. 2. Implemented frame structure (compatible with IEEE 802.15.4-2015 LECIM FSK PHY).

Fig. 3 .

 3 Fig. 3. Bit Error Rate performance of the implemented IEEE 802.15.4-2015 LECIM FSK PHY baseband receiver. The ExpSF4 series is obtained from the SDR-based experimentation platform.

Fig. 4 .Fig. 5 .

 45 Fig. 4. CAS and ACC for two frames transmitted through 'undisturbed' channel

Fig. 7 .

 7 Fig. 7. ROC curves for classifying, respectively, undisturbed versus disturbed channel states (left) and interference versus fading channel states (right).

Fig. 8 .

 8 Fig. 8. Representation of the labelled input training set of ACC with the thresholds λ 1 and λ 2 .

ACKNOWLEDGMENT

The authors thank Franc ¸ois Dehmas for fruitful discussions on DBB implementation as well as Olivier MICHEL and Pierre COMON for the provision of the RIESCO experimentation platform within GIPSA-LAB. This work was supported by the LabEx PERSYVAL-Lab (ANR-11LABX-0025-01).