Involving Users to Improve the Collaborative Logical Framework

In order to support collaboration in web-based learning, there is a need for an intelligent support that facilitates its management during the design, development, and analysis of the collaborative learning experience and supports both students and instructors. At aDeNu research group we have proposed the Collaborative Logical Framework (CLF) to create effective scenarios that support learning through interaction, exploration, discussion, and collaborative knowledge construction. This approach draws on artificial intelligence techniques to support and foster an effective involvement of students to collaborate. At the same time, the instructors’ workload is reduced as some of their tasks—especially those related to the monitoring of the students behavior—are automated. After introducing the CLF approach, in this paper, we present two formative evaluations with users carried out to improve the design of this collaborative tool and thus enrich the personalized support provided. In the first one, we analyze, following the layered evaluation approach, the results of an observational study with 56 participants. In the second one, we tested the infrastructure to gather emotional data when carrying out another observational study with 17 participants

An Evaluation of Mouse and Keyboard Interaction Indicators towards Non-intrusive and Low Cost Affective Modeling in an Educational Context

AbstractIn this paper we propose a series of indicators, which derive from user's interactions with mouse and keyboard. The goal is to evaluate their use in identifying affective states and behavior changes in an e-learning platform by means of non-intrusive and low cost methods. The approach we have followed study user's interactions regardless of the task being performed and its presentation, aiming at finding a solution applicable in any domain. In particular, mouse movements and clicks, as well as keystrokes were recorded during a math problem solving activity where users involved in the experiment had not only to score their degree of valence (i.e., pleasure versus displeasure) and arousal (i.e., high activation versus low activation) of their affective states after each problem by using the Self-Assessment-Manikin scale, but also type a description of their own feelings. By using that affective labeling, we evaluated the information provided by these different indicators processed from the original user's interactions logs. In total, we computed 42 keyboard indicators and 96 mouse indicators

Conditional IMS learning design generation using User modeling and planning techniques

Active modeling is required in learning settings to cope with the dynamic evolution of the knowledge, since learners competences evolves over time as they participate in the course activities. Moreover, one of the main issues in a competence based eLearning process is to deliver personalized instructional designs adjusted to both 1) intrinsic characteristics of users (i.e. learning styles) and 2) the desired and achieved competences in the learning process (i.e. specific and generic competences). This delivery includes the adaptation of the content and the activities in a learning scenario based on a dynamic user model that evolves according to user interactions. In this paper, an approach to support Conditional Plans Generation (IMS Learning Designs) in the context of a virtual learning environment is presented. The process is supported by a pervasive usage of standards and specifications (IMS family of specifications) in conjunction with an integral user modeling

Stakeholder Perspectives on the Ethics of AI in Distance-Based Higher Education

Increasingly, Artificial Intelligence (AI) is having an impact on distance-based higher education, where it is revealing multiple ethical issues. However, to date, there has been limited research addressing the perspectives of key stakeholders about these developments. The study presented in this paper sought to address this gap by investigating the perspectives of three key groups of stakeholders in distance-based higher education: students, teachers, and institutions. Empirical data collected in two workshops and a survey helped identify what concerns these stakeholders had about the ethics of AI in distance-based higher education. A theoretical framework for the ethics of AI in education was used to analyse that data and helped identify what was missing. In this exploratory study, there was no attempt to prioritise issues as more, or less, important. Instead, the value of the study reported in this paper derives from (a) the breadth and detail of the issues that have been identified, and (b) their categorisation in a unifying framework. Together these provide a foundation for future research and may also usefully inform future institutional implementation and practice

Accessible lifelong learning at higher education:outcomes and lessons Learned at two different PilotSites in the EU4ALL Project

[EN] The EU4ALL project (IST-FP6-034778) has developed a general framework to address the needs of accessible lifelong learning at Higher Education level consisting of several standards-based interoperable components integrated into an open web service architecture aimed at supporting adapted interaction to guarantee students' accessibility needs. Its flexibility has supported the project implementation at several sites with different settings and various learning management systems. Large-scale evaluations involving hundreds of users, considering diverse disability types, and key staff roles have allowed obtaining valuable lessons with respect to "how to adopt or enhance eLearning accessibility" at university. The project was evaluated at four higher education institutions, two of the largest in Europe and two mediumsized. In this paper, we focus on describing the implementation and main conclusions at the largest project evaluation site (UNED), which was involved in the project from the beginning, and thus, in the design process, and a medium-sized university that adopted the EU4ALL approach (UPV). This implies dealing with two well-known open source learning environments (i.e. dotLRN and Sakai), and considering a wide variety of stakeholders and requirements. Thus the results of this evaluation serve to illustrate the coverage of both the approach and developments.The authors would like to thank the European Commission for the financial support of the EU4ALL project (IST-2006-034478). The work at aDeNu is also supported by the Spanish Ministry of Science and Innovation (TIN2008-06862-C04-01/TSI “A2UN@”). Authors would also like to thank all the EU4ALL partners for their collaboration.Boticario, JG.; Rodriguez-Ascaso, A.; Santos, OC.; Raffenne, E.; Montandon, L.; Roldán Martínez, D.; Buendía García, F. (2012). Accessible lifelong learning at higher education:outcomes and lessons Learned at two different PilotSites in the EU4ALL Project. Journal of Universal Computer Science. 18(1):62-85. http://hdl.handle.net/10251/37117628518

Developing adaptive learning management systems - An open IMS-based user modelling approach

Adaptive LMS have not yet reached the eLearning marketplace due to methodological, technological and management open issues. At aDeNu group, we have been working on two key challenges for the last five years in related research projects. Firstly, develop the general framework and a running architecture to support the adaptive life cycle (i.e., design, publication, use and monitoring), which focuses on a user-centred experience. Secondly, construct the required models based on standards to support adaptive learning scenarios which combine IMS-based design solutions and intelligent analysis of users' interactions. In this paper we describe the design rationale of the developed architecture, the user modelling approach and the main experimentation results at aLFanet project. Furthermore, we introduce our current research works on key open issues i) automatic generation of IMS-LD designs (ADAPTAPlan) and ii) how to extend those models to cope with accessibility and functional diversity issues to provide services for "all", which take into account pedagogical and psychological issues (EU4ALL).Editors: Daniel Burgos

Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios

This paper analyzes how recommender systems can be applied to current e-learning systems to guide learners in personalized inclusive e-learning scenarios. Recommendations can be used to overcome current limitations of learning management systems in providing personalization and accessibility features. Recommenders can take advantage of standards-based solutions to provide inclusive support. To this end we have identified the need for developing semantic educational recommender systems, which are able to extend existing learning management systems with adaptive navigation support. In this paper we present three requirements to be considered in developing these semantic educational recommender systems, which are in line with the service-oriented approach of the third generation of learning management systems, namely: (i) a recommendation model; (ii) an open standards-based service-oriented architecture; and (iii) a usable and accessible graphical user interface to deliver the recommendations

Issues in developing adaptive learning management systems for higher education institutions

Abstract. Adaptive Learning Management Systems (aLMS) have not yet reached the eLearning marketplace to provide life long professional development. Existing difficulties related to coping with generic learning processes cover methodological, technological, and management issues. Based on our experience in aLFanet (IST-2001-33288), in this paper we review relevant issues arisen when developing an aLMS based on a pervasive use of educational standards (IMS-LD, IMS-CP, IEEE-LOM/IMS-MD, IMS-LIP, IMS-QTI). In particular, we present the aLFanet approach and comment on the consequences of applying such type of systems in real large-scale situations that take place at mega-universities like UNED.