CAMera for PLE

Schmitz, Hans-Christian; Scheffel, Maren; Friedrich, Martin; Jahn, Marco; Niemann, Katja; Wolpers, Martin

doi:10.1007/978-3-642-04636-0_47

Cited by 36 publications

(33 citation statements)

References 14 publications

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“…on learning resources, communities, groups, etc. The ODS data complies with the CAM (Context Automated Metadata) format [14], which provides a standard metadata specification for collecting and storing social data. A CAM schema aims to store whatever has attracted users' attention while the users are working with the platform.…”

Section: Datamentioning

confidence: 99%

User-Centric Evaluation of Recommender Systems in Social Learning Platforms: Accuracy is Just the Tip of the Iceberg

Fazeli

Drachsler

Bitter-Rijpkema

et al. 2018

IEEE Trans. Learning Technol.

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Abstract-Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But the ultimate goal of a recommender system is to increase user satisfaction. Therefore, evaluations that measure user satisfaction should be also performed before deploying a recommender system to a real target environment. Such evaluations are laborious and complicated compared to the traditional, data-centric evaluations, though. In this study, we investigate the added value of user-centric evaluations and how user satisfaction of a recommender system is related to its performance in terms of accuracy metrics. We conduct both a data-centric evaluation and a user-centric evaluation on the same data collected from an authentic social learning platform. Our findings suggest that user-centric evaluation results are not necessarily in line with data-centric evaluation results. We conclude that the traditional evaluation of recommender systems in terms of prediction accuracy does not suffice to judge performance of recommender systems on the user side. Moreover, the user-centric evaluation provides valuable insights on how candidate algorithms perform on each of the five quality metrics: usefulness, accuracy, novelty, diversity, and serendipity of the recommendations.

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Section: Datamentioning

confidence: 99%

User-Centric Evaluation of Recommender Systems in Social Learning Platforms: Accuracy is Just the Tip of the Iceberg

Fazeli

Drachsler

Bitter-Rijpkema

et al. 2018

IEEE Trans. Learning Technol.

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“…Being aware of one's own activities is a prerequisite for reflection. In [2], we have shown that one way of helping a learner to become aware of his actions and learning processes is to record and store his interaction with his computer and to then analyse the collected data. Results of these analyses can on the one hand be used to foster his self-reflection processes or on the other hand to give recommendations, e.g.…”

Section: User Monitoring and Learning Analyticsmentioning

confidence: 99%

Key Action Extraction for Learning Analytics

Scheffel

Niemann

Leony

et al. 2012

Lecture Notes in Computer Science

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Abstract. Analogous to keywords describing the important and relevant content of a document we extract key actions from learners' usage data assuming that they represent important and relevant parts of their learning behaviour. These key actions enable the teachers to better understand the dynamics in their classes and the problems that occur while learning. Based on these insights, teachers can intervene directly as well as improve the quality of their learning material and learning design. We test our approach on usage data collected in a large introductory C programming course at a university and discuss the results based on the feedback of the teachers.

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“…In the field of Technology Enhanced Learning, monitoring student activity has been proposed as a tool for promoting self-reflection [10,22], awareness [10,14], student assessment [14], course management [15], course evaluation [25], and, of course, learning adaptation and personalization [26], among other potential applications.…”

Section: Related Workmentioning

confidence: 99%

Automatic Discovery of Complementary Learning Resources

Zaldivar

García

Burgos

et al. 2011

Towards Ubiquitous Learning

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Abstract. Students in a learning experience can be seen as a commu-nity working simultaneously (and in some cases collaboratively) in a set of activities. During these working sessions, students carry out nu-merous actions that affect their learning. But those actions happening outside a class or the Learning Management System cannot be easily observed. This paper presents a technique to widen the observability of these actions. The set of documents browsed by the students in a course was recorded during a period of eight weeks. These documents are then processed and the set with highest similarity with the course notes are selected and recommended back to all the students. The main problem is that this user community visits thousands of documents and only a small percent of them are suitable for recommendation. Using a combination of lexican analysis and information retrieval techniques, a fully automatic procedure to analyze these documents, classify them and select the most relevant ones is presented. The approach has been validated with an em-pirical study in an undergraduate engineering course with more than one hundred students. The recommended resources were rated as "relevant to the course" by the seven instructors with teaching duties in the course.

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CAMera for PLE

Cited by 36 publications

References 14 publications

User-Centric Evaluation of Recommender Systems in Social Learning Platforms: Accuracy is Just the Tip of the Iceberg

User-Centric Evaluation of Recommender Systems in Social Learning Platforms: Accuracy is Just the Tip of the Iceberg

Key Action Extraction for Learning Analytics

Automatic Discovery of Complementary Learning Resources

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