60,500 research outputs found

    Security in online learning assessment towards an effective trustworthiness approach to support e-learning teams

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    (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This paper proposes a trustworthiness model for the design of secure learning assessment in on-line collaborative learning groups. Although computer supported collaborative learning has been widely adopted in many educational institutions over the last decade, there exist still drawbacks which limit their potential in collaborative learning activities. Among these limitations, we investigate information security requirements in on-line assessment, (e-assessment), which can be developed in collaborative learning contexts. Despite information security enhancements have been developed in recent years, to the best of our knowledge, integrated and holistic security models have not been completely carried out yet. Even when security advanced methodologies and technologies are deployed in Learning Management Systems, too many types of vulnerabilities still remain opened and unsolved. Therefore, new models such as trustworthiness approaches can overcome these lacks and support e-assessment requirements for e-Learning. To this end, a trustworthiness model is designed in order to conduct the guidelines of a holistic security model for on-line collaborative learning through effective trustworthiness approaches. In addition, since users' trustworthiness analysis involves large amounts of ill-structured data, a parallel processing paradigm is proposed to build relevant information modeling trustworthiness levels for e-Learning.Peer ReviewedPostprint (author's final draft

    Accommodating the difference in students’ prior knowledge of cell growth kinetics

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    This paper describes the development and benefits of an adaptive digital module on cell growth to tackle the problem of educating a heterogeneous group of students at the beginning of an undergraduate course on process engineering. Aim of the digital module is to provide students with the minimal level of knowledge on cell growth kinetics they need to comprehend the content knowledge of the subsequent lectures and pass the exam. The module was organised to offer the subject matter in a differentiated manner, so that students could follow different learning paths. Two student groups were investigated, one consisting of students who had received their prior education abroad and one of students that had not. Exam scores, questionnaires, and logged user data of the two student groups were analysed to discover whether the digital module had the intended effect. The results indicate that students did indeed follow different learning paths. Also, the differences in exam scores between the two student groups that was present before the introduction of the digital module was found to have decreased afterwards. In general, students appreciated the use of the material regardless of their prior education. We therefore conclude that the use of adaptive digital learning material is a possible way to solve the problem of differences in prior education of students entering a course

    Supporting mediated peer-evaluation to grade answers to open-ended questions

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    We show an approach to semi-automatic grading of answers given by students to open ended questions (open answers). We use both peer-evaluation and teacher evaluation. A learner is modeled by her Knowledge and her assessments quality (Judgment). The data generated by the peer- and teacher- evaluations, and by the learner models is represented by a Bayesian Network, in which the grades of the answers, and the elements of the learner models, are variables, with values in a probability distribution. The initial state of the network is determined by the peer-assessment data. Then, each teacher’s grading of an answer triggers evidence propagation in the network. The framework is implemented in a web-based system. We present also an experimental activity, set to verify the effectiveness of the approach, in terms of correctness of system grading, amount of required teacher's work, and correlation of system outputs with teacher’s grades and student’s final exam grade

    Higher education decision making and decision support systems

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    The authors illustrate several issues in decision support and decision support systems (DSS), state of the art research in these fields, and also their own studies in designing a higher education DSS. The final section contains our contribution in outlining the modules of the DSS, involving the present systems and databases of FSEGA and UBB, results and activities belonging to FSEGA students, teaching and research staff, to assist decisions for all the actors implicated in the processes, in various specific situations.decision support, decision support systems (DSS), higher education institutions, Information and Communication Technologies (ICT)

    Creating a climate of intelligent accountability in all-through academies

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    Impacto del desempeño de los estudiantes en una metodología de evaluación continua en Moodle en el examen final

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    Este trabajo examina la diferente evolución del rendimiento de los estudiantes en cuestionarios online y su impacto en la calificación final. Esta innovadora técnica se ha utilizado en un grupo de un curso introductorio de contabilidad financiera con 8 cuestionarios online (uno por unidad temática) a través de la plataforma Moodle. Empleando el análisis cluster, identificamos diferentes grupos de evolución del rendimiento. La evidencia obtenida sugiere que en uno de estos grupos una evolución favorable del rendimiento en los test online puede conducir a un exceso de confianza, con el posterior efecto negativo en la nota del examen final. La investigación futura con más variables y muestras de mayor tamaño ayudará a identificar este perfil de estudiante con el fin de evitar un posible efecto negativo no deseado de esta técnica de enseñanza.This paper looks into the different evolution of students’ online questionnaire performance and its impact on the final examination mark. This innovative technique has been used in a group of an introductory financial accounting course with 8 online questionnaires (one per unit) in the Moodle platform. Using cluster analysis, we identify different groups of performance evolution. The evidence obtained suggests that in one of this groups a favourable test performance evolution may lead to overconfidence with the subsequent negative effect on the final examination mark. Future research with more variables and bigger samples will help to identify this student profile with a view to prevent this undesired negative effect of this teaching technique
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