A semantic approach to mobile learning analytics

Fulantelli, Giovanni; Taibi, Davide; Arrigo, Marco

doi:10.1145/2536536.2536579

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…Two case studies integrating GradeCraft into a videogame class and a political science course. Fulantelli, Taibi, and Arrigo (2013) Describes the LA platform of, a mobile learning environment, MeLOD, for teachers to identify preferred activities and monitor students' progress.…”

Section: Student Progress Is Monitored Based On Four Modelsmentioning

confidence: 99%

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Specification and evaluation of an assessment engine for educational games: Empowering educators with an assessment editor and a learning analytics dashboard

Chaudy

Connolly

2018

Entertainment Computing

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Assessment is a crucial aspect of any teaching and learning process. Educational games offer promising advantages for assessment; personalised feedback to students and automated assessment process. However, while many teachers agree that educational games increase motivation, learning and retention, few of them are ready to fully trust them as an assessment tool. We believe there are two main reasons for this lack of trust: educators are not given sufficient information about the gameplays, and many educational games are distributed as black-boxes, unmodifiable by teachers. This paper presents an assessment engine designed to separate a game and its assessment. It allows teachers to modify a game's assessment after distribution and visualise gameplay data via a learning analytics dashboard. The engine was evaluated quantitatively by 31 educators. Findings were overall very positive: both the assessment editor and the learning analytics dashboard were rated useful and easy to use. The evaluation also indicates that, having access to EngAGe, educators would be more likely to trust a game's assessment. This paper concludes that EngAGe can be used by educators effectively to modify educational games' assessment and visualise gameplay data, and that it contributes to increasing their trust in educational games as an assessment tool.

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Section: Student Progress Is Monitored Based On Four Modelsmentioning

confidence: 99%

“…The information can be demographic (e.g. age, gender, language), academic (Serrano-Laguna & Fernandez-Manjon, 2014) or technical with system configuration and session data being logged (Fulantelli et al, 2013).…”

Section: Different Types Of Data Collectedmentioning

confidence: 99%

Specification and evaluation of an assessment engine for educational games: Empowering educators with an assessment editor and a learning analytics dashboard

Chaudy

Connolly

2018

Entertainment Computing

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“…We chose this type of representation because of its ability to represent single values and at the same time to set meaningful boundaries that can be easy to understand and interpret. Gauges have been repeatedly used in critical interfaces such as cars and cockpit panels because of their ability to deliver a straightforward message (Hutchins, 1995), and have also been used successfully in learning analytics visualizations (de la Fuente Valentín & Solans, 2014;Falakmasir, Hsiao, Mazzola, Grant, & Brusilovsky, 2012;Fulantelli, Taibi, & Arrigo, 2013;Khan & Pardo, 2016). As said before, the learning gauge does not add extra information, as the social comparison feature does, but instead presents an interpretive view of the information already shown in the visualization.…”

Section: The R-olmmentioning

confidence: 99%

Navigation support in complex open learner models: assessing visual design alternatives

Guerra

Schunn

Bull³

et al. 2018

New Review of Hypermedia and Multimedia

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Open Learner Models are used in modern e-learning to show system users the content of their learner models. This approach is known to prompt reflection, facilitate planning and navigation. Open Learner Models may show di↵erent levels of detail of the underlying learner model, and may structure the information di↵erently. However, a trade-o↵ exists between useful information and the complexity of the information. This paper investigates whether o↵ering richer information is assessed positively by learners and results in more e↵ective support for learning tasks. An interview pre-study revealed which information within the complex learner model is of interest. A controlled user study examined six alternative visualization prototypes of varying complexity and resulted in the implementation of one of the designs. A second controlled study involved students interacting with variations of the visualization while searching for suitable learning material, and revealed the value of the design alternative and its variations. The work contributes to developing complex open learner models by stressing the need to balance complexity and support. It also suggests that the expressiveness of open learner models can be improved with visual elements that strategically summarize the complex information being displayed in detail.

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“…e second version (KCG (KC + Gauge)) in Figure 5 adds a visual aid speci cally designed to direct the interpretation of the information displayed by the KC visualization towards a sense of the relevance of each of the activities within a topic. Gauges are popular to represent single values and at the same time se ing meaningful boundaries, and have also been used in learning analytics visualizations [10,12,14,20]. We then designed the estimated learning gauge, or simply, gauge.…”

Section: Study 2 Designmentioning

confidence: 99%

Fine-Grained Open Learner Models

Guerra-Hollstein

Barria-Pineda

Schunn

et al. 2017

Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

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Open Learner Models (OLM) show the learner model to users to assist their self-regulated learning by, for example, helping prompt re ection, facilitating planning and supporting navigation. OLMs can show di erent levels of detail of the underlying learner model, and can also structure the information di erently. As a result, a trade-o may exist between the potential for be er support for learning and the complexity of the information shown. is paper investigates students' perceptions about whether o ering more and richer information in an OLM will result in more e ective support for their self-regulated learning. In a rst study, questionnaire responses relating to designs for six visualisations of varying complexity led to the implementation of three variations on one of the designs. A second controlled study involved students interacting with these variations. e study revealed that the most useful variation for searching for suitable learning material was a visualisation combining a basic coloured grid, an extended bar chart-like visualisation indicating related concepts, and a learning gauge.

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A semantic approach to mobile learning analytics

Cited by 19 publications

References 10 publications

Specification and evaluation of an assessment engine for educational games: Empowering educators with an assessment editor and a learning analytics dashboard

Specification and evaluation of an assessment engine for educational games: Empowering educators with an assessment editor and a learning analytics dashboard

Navigation support in complex open learner models: assessing visual design alternatives

Fine-Grained Open Learner Models

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