12,949 research outputs found
Critical Needs and Demands for IT-based support to distance learning and collaborative work in European companies.
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SAW: Safeness at the workplace. Report on the course "Sicurezza sul posto di lavoro: Legge 626
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Correcting open-answer questionnaires through a Bayesian-network model of peer-based assessment
No full textWe have previously shown that, with the help of peer-assessment and of a finite-domain constraint-based model of the student's decisions, the teacher could have a complete assessment of the answers to open-ended questions, by grading just a subset of the answers (as low as half of the lot) and having the rest of the grading inferred by the supporting system. In this paper we present a probabilistic version of the earlier model, using Bayesian networks instead than constraints. Our aims are both defining the approach and prepare its validation: 1) modeling the peer-assessment activity of a student that evaluates others' answers, 2) using peer-assessment to help the teacher with a faster/shorter assessment process, 3) inferring the student's level of competence and ability to judge, from peer-assessment and from (partial) teacher-assessment, 4) learning the conditional probabilistic tables (CPTs) of the model from student data, and 5) comparing the probability distribution of competences in the class at different course phases. The model is under development and test with real data. We are developing a web-based interface to deliver open-answer and peer-assessment questionnaires and to assist the teacher-assessment. © 2012 IEEE
Social exchange and collaboration in a reputation-based educational system
No full textThe present state of the implementation of SOCIALX is presented. It is a web-based reputation system, supporting collaborative and social aspects of e-learning. The system supports 1) sharing and exchange of solutions to exercises, 2) discussion of such solutions through forum, and 3) project activity (projects are divided into steps and the groups can work in the usual collaborative manner, yet their products are to be used and evaluated by other groups in their project-steps; groups can also self-evaluate their work in each step). Participation and collaboration in the system are supported also by contextual (to the exercise) FAQs and micro-forums, and encouraged by the management of a currency-based representation of the value of contributions coming by the students (such value is interpretable as the usefulness of a product contribution or of an answer to question proposed by other students). In particular tokens are exchanged by giving and answering questions in the forum. Besides representing the richness of one's participation in the system, tokens can be used also to help the teacher/tutor in electing the most suitable question/answer pairs to be promoted into the FAQ (collection of Frequently Asked Questions). Group activities in the projects are regulated by self/peer-evaluated phases, in order to favour group responsibility, manage with peer-pressure and allow self-evaluation. In particular, the different phases of a project are given to different groups, and the product of a group can be self-evaluated (at submission) and peer-evaluated (by the group that receives it to continua with a next phase). ©2010 IEEE
Analysis of open answers via mediated peer-assessment
No full textThe analysis and grading of open answers (i.e. answers to open-ended questions) is a powerful means to model the state of knowledge and cognitive level of students in e-learning systems. In a previous work (presented at last SPEL workshop) we showed an approach to open answers grading, based on Constraint Logic Programming (CLP) and peer assessment, where students were defined as triples of finite-domain variables: K for student's Knowledge about the question's topic, C for Correctness of her/his answer, and J for her/his estimated ability to evaluate ("Judge") another peer's answer. The CLP Prolog module supported the grading process helping eventually to get a complete set of grades although the teacher had actually graded only a (substantial indeed) part of them. Here we try and tackle the problem of grading open answers by an alternative approach, using peer-assessment in a social collaborative e-learning setting, mediated by the teacher through a simple Bayesian-networks-based model, that allows managing student models (based on the same finite-domain variables as above) and producing again automated evaluations of those answers that have not been graded by the teacher. In particular we give an account of the OpenAnswer web-based system, which can allow teachers and students to use our approach, and show the result of some experimentation we conducted
Supporting assessment of open answers in a didactic setting
No full textThe Open Answers module is designed to be integrated into the social collaborative reputation-based elearning system SocialX, to manage answers to open questions. In particular, its aim is to support personal evaluation of skills and knowledge of students, involved in peer-assessment-based learning activities, while mitigating the workload imposed upon the teacher, for analysis and correction of the answers. In brief, 1) students do answer open questions, to be evaluated by peers and teacher; 2) students peer-evaluate each other's answers; 3) the teacher grades only a subset of the whole answers corpus; 4) the system infers the assessment for the remaining answers, by exploiting the relations established through the web of the students' peer-assessments of those answers, and the personal evaluation maintained for each student. The peer-assessment data are analyzed through a constraint-logic-based model of student's possible behaviors, obtaining a (possibly big!) set of hypotheses on the answers' correctness. The teacher is proposed with a minimum set of answers to grade: by such grading (s)he helps narrowing the set of hypotheses. Testing of the constraint-logic-based analysis engine is ongoing by means of simulation devices that generate suitable sets of students' behaviors. © 2012 IEEE
Learning from peers: Motivating students through reputation systems
No full textOur 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 other's solutions or by correcting other's 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 [1]. © 2008 IEEE
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