Teachers’ Perspectives on the Promises, Needs and Challenges of Learning Analytics Dashboards: Insights from Institutions Offering Blended and Distance Learning

Kaliisa, Rogers; Gillespie, Anna; Herodotou, Christothea; Kluge, Anders; Rienties, Bart

doi:10.1007/978-3-030-81222-5_16

Cited by 11 publications

(8 citation statements)

References 39 publications

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“…First, while dashboard has been considered as a relatively straightforward solution that generates actional insights into learning (Kaliisa et al, 2021), there has been a lack of teacher-facing dashboard solutions aimed at improving LD activities (Sahin & Ifenthaler, 2021). Our study therefore serves as an exploration into this issue, providing a possible outlook of a visualization dashboard for learning design purposes.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore widespread use of LA in daily teaching practice is mainly seen in STEM disciplines, where staff have adequate technical competence (M. Liu et al, 2019) and where specific funding has been supplied (Tsai et al, 2019). Most staff, on the other hand, require training and support to use LA (Kaliisa et al, 2021;Tsai & Gasevic, 2017) and perceive institutional learning platforms where most learning-related data are stored as being unable to produce readily interpretable analytics (Lockyer et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Analytics-Informed Design: Exploring Visualization of Learning Management Systems Recorded Data for Learning Design

Liu,

Gladman,

Muir

et al. 2023

Sage Open

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One apparent challenge associated with learning analytics (LA) has been to promote adoption by university educators. Researchers suggest that a visualization dashboard could serve to help educators use LA to improve learning design (LD) practice. We therefore used an educational design approach to develop a pedagogically useful and easy-to-use LA visualization solution to support data-informed LD. We interviewed four staff in a medical degree program at a New Zealand university, designed and piloted a dashboard, and evaluated it through interviews. As a proof-of-concept project, our study showed that educational design research could be meaningfully used to develop a visualization dashboard that is easy-to-use and useful. In particular, the preliminary design principles identified provide implications for practitioners who are seeking to use LA to inform LD. Finally, we reflect on the purpose of visualization dashboards in relation to the literature and identify areas for future developments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analytics-Informed Design: Exploring Visualization of Learning Management Systems Recorded Data for Learning Design

Liu,

Gladman,

Muir

et al. 2023

Sage Open

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“…The clustering approach appears to be a robust analytics strategy for obtaining meaningful insights into the frequent navigation behaviour (Poon et al, 2017). Making wider use of such learning analytics data through interactive dashboards may assist facilitators in their pedagogical decision‐making throughout the semester (Arthars et al, 2019; Kaliisa et al, 2021). As students in both clusters did not show high transitions from self‐assessments and lecture recordings and vice versa, adaptive recommendations on related learning material and suggested next steps after having taken a self‐assessment or after having watched the lecture recordings may be beneficiary (Bodily et al, 2018) and could be further investigated.…”

Section: Discussionmentioning

confidence: 99%

Investigating students' use of self‐assessments in higher education using learning analytics

Schumacher

Kužílek

2022

Computer Assisted Learning

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Background: Formative assessments are vital for supporting learning and performance but are also considered to increase the workload of teachers. As selfassessments in higher education are increasingly facilitated via digital learning environments allowing to offer direct feedback and tracking students' digital learning behaviour these constraints might be reduced. Yet, learning analytics do not make sufficient use of data on assessments.Aims: This exploratory case study uses learning analytics methods for investigating students' engagement with self-assessments and how this relates to performance in the final exam and self-reported self-testing strategies. Materials & Methods:The research study has been conducted at a European university in a twelve-weeks course of a Bachelor's program in Economic and Business Education including n enroll = 159 participants. During the semester, students were offered nine self-assessments each including three to eight tasks plus one mid-term and one exam-preparation self-assessment including all prior self-assessments tasks.The self-assessment interaction data for each student included: the results of the last self-assessment attempt, the number of processed self-assessment tasks, and the time spent on the last self-assessment attempt, the total self-assessment attempts, and the first as well as last access of each self-assessment. Data analytics included unsupervised machine learning and process mining approaches.Results: Findings indicate that students use the self-assessments predominantly before summative assessments. Two distinct clusters based on engagement with self-assessments could be identified and engagement was positively related to performance in the final exam. The findings from learning analytics data were also indicated by students' self-reported use of self-testing strategies.Discussion: With the help of multiple data from self-reports, formal exams, and a learning analytics system, the findings provided multiple perspectives on the use of self-assessments and their relationships with course performance. These findings call Dirk Ifenthaler and Clara Schumacher contributed equally to this work and share the first authorship.

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“…On the other hand, the use of extensive sensor data might help teachers monitor their students' learning progress [102], [103]. Viewing the collected data on supportive tools, such as LA dashboards, affords teachers new insights, which help them make informed data-driven learning decisions by providing formative and summative feedback; however, additional effort is needed to ensure that the produced LA are effectively designed [104], [105], [106]. Chatti et al [44] have already discussed the role of both students and teachers in their framework, and involving them in the design process has the potential to increase the acceptance and adoption of LA solutions.…”

Section: The Important Role Of the Secondary User In Multisensory Env...mentioning

confidence: 99%

Multisensory Interaction and Analytics to Enhance Smart Learning Environments: A Systematic Literature Review

Cosentino

Giannakos

2023

IEEE Trans. Learning Technol.

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Smart learning environments (SLEs) leverage technological developments to enable effective, engaging and personalized learning. SLEs rely on sensor and advanced interconnectivity capabilities to infer and reason. A natural set of affordances for SLEs are those of Multisensory Environments (MSEs). MSEs enable humans to make full use of their senses and equip learning systems with new information, methods of information exchange, and intelligent capabilities. In addition to novel forms of interaction, MSEs also offer novel forms of "learner traces" through Multimodal Learning Analytics (MMLA). This paper presents the results of a systematic literature review on how multisensory interactions and the respective analytics can support the use and design of SLEs. The findings from the analysis of 33 papers synthesize and clarify the latest advancements in the intersection of interactions and analytics in MSEs, discuss how those advancements can support SLEs' affordances and uses, and pave the way for improving our understanding of how various interaction modalities can support learning and under what conditions.

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Teachers’ Perspectives on the Promises, Needs and Challenges of Learning Analytics Dashboards: Insights from Institutions Offering Blended and Distance Learning

Cited by 11 publications

References 39 publications

Analytics-Informed Design: Exploring Visualization of Learning Management Systems Recorded Data for Learning Design

Analytics-Informed Design: Exploring Visualization of Learning Management Systems Recorded Data for Learning Design

Investigating students' use of self‐assessments in higher education using learning analytics

Multisensory Interaction and Analytics to Enhance Smart Learning Environments: A Systematic Literature Review

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