Dutch Cooking with xAPI Recipes: The Good, the Bad, and the Consistent

Berg, Alan; Scheffel, Maren; Drachsler, Hendrik; Ternier, Stefaan; Specht, Marcus

doi:10.1109/icalt.2016.48

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…The solution discussed in section 3.6 consisted in using the Linear Mixed Effect Models as they allow (1) taking into account data specific to each learner; (2) distinguishing between fixed and random effects; (3) taking categorical data into account. Despite LMEM being the appropriate model for the intended task, the R-squared evaluation test yielded poor prediction accuracies for the five outputs.…”

Section: Discussionmentioning

confidence: 99%

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Learning pulse

Mitri

Scheffel

Drachsler

et al. 2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

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Learning Pulse explores whether using a machine learning approach on multimodal data such as heart rate, step count, weather condition and learning activity can be used to predict learning performance in self-regulated learning settings. An experiment was carried out lasting eight weeks involving PhD students as participants, each of them wearing a Fitbit HR wristband and having their application on their computer recorded during their learning and working activities throughout the day. A software infrastructure for collecting multimodal learning experiences was implemented. As part of this infrastructure a Data Processing Application was developed to pre-process, analyse and generate predictions to provide feedback to the users about their learning performance. Data from different sources were stored using the xAPI standard into a cloud-based Learning Record Store. The participants of the experiment were asked to rate their learning experience through an Activity Rating Tool indicating their perceived level of productivity, stress, challenge and abilities. These self-reported performance indicators were used as markers to train a Linear Mixed Effect Model to generate learner-specific predictions of the learning performance. We discuss the advantages and the limitations of the used approach, highlighting further development points.

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Section: Discussionmentioning

confidence: 99%

“…The main advantage of using xAPI is interoperability: learning data from any system or resource can be captured and eventually queried by the third party authenticated services. For each event captured in Learning Pulse, an xAPI statement template was designed following the Dutch xAPI specification for learning activities [2] 5 .…”

Section: Experience Apimentioning

confidence: 99%

Learning pulse

Mitri

Scheffel

Drachsler

et al. 2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

Self Cite

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“…Standards for data models and data collection in e-learning are still lacking [6], [7] and pose a barrier to the scaling and interoperability of LA. Our proposed pipeline is optimized to work with LA data conforming to the Experience API (xAPI) standard [8] in the JSON format, but is also adaptable to work with LA specifications and formats from other platforms.…”

Section: A Data Formats and Interoperabilitymentioning

confidence: 99%

“…Provided consent has been given, the platform's learning spaces and the applications within them capture LA data conforming to the Experience API (xAPI) [8] standard. Predefined user interactions with a learning space or application, such as navigation between lessons or the typing of text into an input box, generate data points known as actions, which are sent to and stored on the platform's servers.…”

Section: Data Gatheringmentioning

confidence: 99%

An End-to-End Data Pipeline for Managing Learning Analytics

Farah

Machado

Cunha

et al. 2021

2021 19th International Conference on Information Technology Based Higher Education and Training (ITHET)

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Despite the importance of learning analytics in digital education, there is limited support for researchers in education to generate, access, and share experimental data while complying with ethical and privacy legislation. We propose a set of related tools that support researchers with these tasks and present a blueprint for how these tools can be integrated with existing platforms, enabling researchers to run studies within learning environments, adhere to legal and ethical privacy frameworks, and share their anonymous or anonymized data with a wider audience. We demonstrate the integration of these features into an existing online learning platform.

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“…Their results also showed that students had different operating habits and learning paths which provided the basis for the evaluation of students' spatial thinking ability and engineering design skill in the interactive learning environment. The authors in [108] discussed some experiences and learnt lessons from implementing xAPI for projects in the Netherlands. The authors remarked on the need for a centralized approach for data collection to get a complete picture of student behavior which may be stored on many heterogeneous IT systems.…”

Section: ) Learning and Assessment Analytics Using Experience Apimentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

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Dutch Cooking with xAPI Recipes: The Good, the Bad, and the Consistent

Cited by 12 publications

References 7 publications

Learning pulse

Learning pulse

An End-to-End Data Pipeline for Managing Learning Analytics

Big Educational Data & Analytics: Survey, Architecture and Challenges

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