Marco Temperini

Adaptive Learning with the LS-Plan System: A Field Evaluation

LS-Plan is a framework for personalization and adaptation in e-learning. In such framework an Adaptation Engine plays a main role, managing the generation of personalized courses from suitable repositories of learning nodes and ensuring the maintenance of such courses, for continuous adaptation of the learning material proposed to the learner. Adaptation is meant, in this case, with respect to the knowledge possessed by the learner and her learning styles, both evaluated prior to the course and maintained while attending the course. Knowledge and Learning styles are the components of the student model managed by the framework. Both the static, precourse, and dynamic, in-course, generation of personalized learning paths are managed through an adaptation algorithm and performed by a planner, based on Linear Temporal Logic. A first Learning Objects Sequence is produced based on the initial learners Cognitive State and Learning Styles, as assessed through prenavigation tests. During the students navigation, and on the basis of learning assessments, the adaptation algorithm can output a new Learning Objects Sequence to respond to changes in the student model. We report here on an extensive experimental evaluation, performed by integrating LS-Plan in an educational hypermedia, the LecompS web application, and using it to produce and deliver several personalized courses in an educational environment dedicated to Italian Neorealist Cinema. The evaluation is performed by mainly following two standard procedures: the As a Whole and the Layered approaches. The results are encouraging both for the system on the whole and for the adaptive components.

Adaptive construction and delivery of web-based learning paths

We present the “Lecomps” web-based e-learning environment, that automates the construction of adaptive personalized learning paths, tailored over learning goals, learners knowledge and individual learning styles. A Constraint Logic based engine is used to perform the course construction and to support the teacher in the definition of the learning objects to be used (Learning Components). Learning Objective templates are used to support the management of Knowledge throughout the framework (in learning components, personal learners traits and course definition).

The Definition of a Tunneling Strategy between Adaptive Learning and Reputation-based Group Activities

We investigate the integration of LECOMPS, a web-based e-learning environment for the automated construction and adaptive delivery of learning paths, and SOCIALX, a web-based system for shared e-learning activities, which exploits a reputation system to provide feedback to its participants. Our overall goal is the integration of personalized and collaborative learning to support the Vygotskijs educational theory of proximal development. Therefore we propose a two-way tunneling strategy: the LECOMPS student model is used to select the set of social activities (met in SOCIALX) according to the present individual learner state of knowledge, on the other hand, the solution of exercises, and the associated reputation derived in SOCIALX, is used to update the LECOMPS student model. In particular, we present a mapping between the student model and the definition of Vygotskijs concepts of Autonomous Problem Solving and Proximal Development regions, with the aim to provide the learner with better guidance during the taking of the course.

Learning from Peers: Motivating Students through Reputation Systems

Our on-line students, being mainly busy worker-students, study almost alone. To improve their interaction we use asynchronous tools (Wiki or forums), but we notice that interaction becomes high mainly when the discussion is focused on a task to be graded for the exam or when the teacher/tutor is very active in the community. We present SOCIALX, our exercise sharing tool, an application to e-learning of a simple reputation system to increase the student motivation and interaction, and to let them learning from each other, either by reusing others solutions or by correcting others mistakes. Moreover, students gain reputation from others reusing their solutions. In this we want to engage students in learning activities at the highest cognitive levels of the Bloom taxonomy.

A Framework to Support Social-Collaborative Personalized e-Learning

We propose a comprehensive framework to support the personalization and adaptivity of courses in e-learning environments where the traditional activity of individual study is augmented by social-collaborative and group based educational activities. The framework aims to get its pedagogical significance from the Vygotskij Theory; it points out a minimal set of requirements to meet, in order to allow its implementations based on modules possibly constituted by independent e-learning software systems, all collaborating under a common interface.

Selection and sequencing constraints for personalized courses

The LecompS framework, for personalized and adaptive e-learning, is recalled. Its enhancements, regarding the selection and sequencing optimization algorithms that can be applied, are shown. Such enhancements are discussed, both in terms of their implementation and with respect to their effectiveness: basing on a stated learners model (the present state of knowledge, and learning styles of the individual learner), and on a definition of course aims (Target Knowledge), we apply the various selection and sequencing algorithms and compare the results; different courses are produced, corresponding to different personalization requirements, that can possibly occur, also in combination.

Supporting Teachers to Retrieve and Select Learning Objects for Personalized Courses in the Moodle_LS Environment

In this paper we present a comprehensive framework supporting the tasks of defining, retrieving, and importing Learning Objects (LOs) for personalized courses. It is partially implemented in a Moodle-based personalization system, where the instructional designer is guided through: 1) a theoretical specification of the needed LOs; 2) a retrieval function of actual LOs, by automatically querying standard-compliant repositories; 3) an analysis of such items, to import those selected by him, also adding metadata relevant to the personalization system, at hand. This work overcomes some well known shortcomings of the Moodle system in supporting retrieval of learning material in a personalization context.

An Ontology-Driven OLAP System to Help Teachers in the Analysis of Web Learning Object Repositories

The Knowledge Society is increasing the demand for tools to manage the didactic knowledge stored in Learning Objects Repositories, and needed by teachers to generate courseware. In this respect, still there is a lack of automated tools for the analysis and retrieval of learning resources from such repositories. Here we propose the use of the OLAP technique to help teachers to specify a didactic ontology by which performing quantitative and qualitative analysis of Internet-based Learning Objects Repositories. The related system is presented, together with a case study based on real repositories.

Collaborative Projects and Self Evaluation within a Social Reputation-Based Exercise-Sharing System

We present the design issues and motivations of an enhanced version of the web-based system SOCIALX, supporting collaborative and social aspects of learning. This web application allows to share solutions to exercises and development of project- (possibly group-) work, through the management of a reputation system. With the aim of enhancing collaboration and to help students working on exercises, we introduce contextual FAQs and micro-forums and a currency-based concretization of the perceived usefulness of other’s answers. The tokens exchanged are used also to help the teacher/tutor in choosing the best question/answer pairs to be promoted to the FAQ. To introduce group responsibilities, peer-pressure and self-evaluation we define group-based projects with self/peer-evaluated phases. The different phases of a project are given to different groups, so that the produced deliverables are both self-evaluated when they are submitted and peer-evaluated by the group working on the next phase. The system is its last stages of development and will be tested with real students in the next academic year.

A recommendation module to help teachers build courses through the Moodle Learning Management System

In traditional e-learning, teachers design sets of Learning Objects (LOs) and organize their sequencing; the material implementing the LOs could be either built anew or adopted from elsewhere (e.g. from standard-compliant repositories) and reused. This task is applicable also when the teacher works in a system for personalized e-learning. In this case, the burden actually increases: for instance, the LOs may need adaptation to the system, through additional metadata. This paper presents a module that gives some support to the operations of retrieving, analyzing, and importing LOs from a set of standard Learning Objects Repositories, acting as a recommending system. In particular, it is designed to support the teacher in the phases of (i) retrieval of LOs, through a keyword-based search mechanism applied to the selected repositories; (ii) analysis of the returned LOs, whose information is enriched by a concept of relevance metric, based on both the results of the searching operation and the data related to the previous use of the LOs in the courses managed by the Learning Management System; and (iii) LO importation into the course under construction.