Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design and analysis of gamified strategies. This paper analysed the game elements employed in gamified learning environments through a previously proposed and evaluated taxonomy while detailing and expanding this taxonomy. In the current paper, we describe our taxonomy in-depth as well as expand it. Our new structured results demonstrate an extension of the proposed taxonomy which results from this process, is divided into five dimensions, related to the learner and the learning environment. Our main contribution is the detailed taxonomy that can be used to design and evaluate gamification design in learning environments.

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Abstract. In spite of the interest in AHS, there are not as many applications as could be expected. We have previously pinpointed the problem to rely on the difficulty of AHS authoring. Adaptive features that have been successfully introduced and implemented until now are often too fine grained, and an author easily looses the overview. This paper introduces a three-layer model and classification method for adaptive techniques: direct adaptation rules, adaptation language and adaptation strategies. The benefits of this model are twofold: on one hand, the granulation level of authoring of adaptive hypermedia can be precisely established, and authors therefore can work at the most suitable level for them. On the other hand, this is a step towards standardization of adaptive techniques, especially by grouping them into a higher-level adaptation language or strategies. In this way, not only adaptive hypermedia authoring, but also adaptive techniques exchange between adaptive applications can be enabled.

Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students’ performance and behaviours, often due to data availability or inadequate granularity. To address this problem, we propose an Online Judge called CodeBench, which allows for fine‐grained data collection of student interactions, at the level of, eg, keystrokes, number of submissions, and grades. We deployed CodeBench for 3 years (2016–18) and collected data from 2058 students from 16 introductory computer science (CS1) courses, on which we have carried out fine‐grained learning analytics, towards early detection of effective/ineffective behaviours regarding learning CS concepts. Results extract clear behavioural classes of CS1 students, significantly differentiated both semantically and statistically, enabling us to better explain how student behaviours during programming have influenced learning outcomes. Finally, we also identify behaviours that can guide novice students to improve their learning performance, which can be used for interventions. We believe this work is a step forward towards enhancing Online Judges and helping teachers and students improve their CS1 teaching/learning practices.

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Technology that can be used for authoring adaptive hypermedia courses. With this tool, the subject-matter of the course to be designed can be modeled by means of concept maps. Based on these concept maps lessons can be constructed. Concept maps and lessons form the two levels of pre-adaptive content, and they are stored in a database. This structure lays the basis for various types of adaptation, as it uses both the expressivity of metadata annotation and the flexibility of the database structure (on which different queries can be performed) as will be illustrated. This paper describes this tool's design, implementation and first evaluation remarks. MOT is being presently used for the creation of a variant of the classical Neural Network course for third year students at the Eindhoven University of Technology.

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(2016) 'Motivational gamication strategies rooted in self-determination theory for social adaptive E-Learning. ', in Intelligent Tutoring Systems, 13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016 Further information on publisher's website:http://dx.doi.org/10.1007/978-3-319-39583-8 3 2Publisher's copyright statement:The nal publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-39583-832Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher's statement:"The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-39583-8_32 A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. Abstract. This study uses gamification as the carrier of understanding the motivational benefits of applying the Self-Determination Theory (SDT) in social adaptive e-learning, by proposing motivational gamification strategies rooted in SDT, as well as developing and testing these strategies. Results show high perceived motivation amongst the students, and identify a high usability of the implementation, which supports the applicability of the proposed approach.