Technological barriers and incentives to learning analytics adoption in higher education: insights from users

Klein, Carrie; Lester, Jaime; Rangwala, Huzefa; Johri, Aditya

doi:10.1007/s12528-019-09210-5

Cited by 46 publications

(56 citation statements)

References 23 publications

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“…Three studies in total presented mixed locations. In more detail, the studies conducted in North America were located in the United States (Jones & VanScoy, 2019;Jones, 2019a;Jones, 2019b;Klein, Lester, Rangwala, & Johri, 2019;Sun, Mhaidli, Watel, Brooks, & Schaub, 2019;Scott & Nichols, 2017). The studies conducted in Europe were located in Germany (Ifenthaler & Schumacher, 2016) and the UK (Adejo & Connolly, 2017;Tsai et al, 2019;Whitelock-Wainwright, Gašević, Tejeiro, Tsai, & Bennett, 2019;Slade, Prinsloo, & Khalil, 2019;.…”

Section: Resultsmentioning

confidence: 99%

“…Regarding the response rate, West et al (2016) reported on an institutional survey with a 55% response rate; Arnold and Sclater (2017) reported on a survey of university students, administered twice, with response rates of 64% and 34%, respectively; Slade et al (2019) The focus groups were structured by questions, audio recorded, and transcribed verbatim (e.g., Roberts et al, 2016;Howell et al, 2018;Brooker et al, 2017). Of the eleven interviews reported, one was conducted in conjunction with participatory observation (Klein et al, 2019).…”

Section: Data Collection Methods Reportedmentioning

confidence: 99%

“…Jones and VanScoy (2019) report the analysis of a course syllabus. Only Klein et al (2019) reported observation data. Only Scott and Nichols (2017) reported clickstream data and usergenerated content collection methods.…”

Section: Data Collection Methods Reportedmentioning

confidence: 99%

“…Among the works applying statistical methods, we observe the use of the statistical package for social analysis (e.g., Ifenthaler & Schumacher, 2016;West et al, 2016;Adejo & Connolly, 2017;Mahroeian et al, 2017), including descriptive (e.g., Arnold & Sclater, 2017), inferential statistics (e.g., Slade et al, 2019), exploratory and confirmatory factor analysis (e.g., Whitelock-Wainwright et al, 2019), as well as Rasch analysis (e.g., West et al, 2018). In the works using open coding, we distinguish open and axial descriptive coding (West et al, 2016(West et al, , 2018Brooker et al, 2017;Klein et al, 2019;Jones, 2019aJones, , 2019bTsai et al, 2019). Thematic analysis was applied to transcriptions (e.g., Roberts et al, 2016;Howell et al, 2018;Mahroeian et al, 2017;Sun et al, 2019;Jones & VanScoy, 2019).…”

Section: Data Analysis Methods Reportedmentioning

confidence: 99%

“…Case studies (5) included those conducted by Lawson et al (2016) and Jones and VanScoy (2019) as well as a hermeneutic multiple case study (Willis, Slade, & Prinsloo, 2016), an instrumental case study (Klein et al, 2019), and a multiple stakeholder study (Sun et al, 2019). They collected data most often via documents (i.e., policy documents and syllabi), interviews, focus groups, and participatory observation.…”

Section: Research Strategies Appliedmentioning

confidence: 99%

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Mapping the Ethics of Learning Analytics in Higher Education: A Systematic Literature Review of Empirical Research

Pargman

McGrath

2021

JLA

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Ethics is a prominent topic in learning analytics that has been commented on from conceptual viewpoints. For a broad range of emerging technologies, systematic literature reviews have proven fruitful by pinpointing research directions, knowledge gaps, and future research work guidance. With these outcomes in mind, we conducted a systematic literature review of the research on ethical issues that have been empirically approached in the learning analytics literature. In our final analysis, 21 articles published in the period 2014–2019 met our inclusion criteria. By analyzing this data, we seek to contribute to the field of learning analytics by 1) characterizing the type of empirical research that has been conducted on ethics in learning analytics in the context of higher education, 2) identifying the main ethical areas addressed in the selected literature, and 3) pinpointing knowledge gaps.

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Section: Resultsmentioning

confidence: 99%

Section: Data Collection Methods Reportedmentioning

confidence: 99%

Section: Data Collection Methods Reportedmentioning

confidence: 99%

Section: Data Analysis Methods Reportedmentioning

confidence: 99%

Section: Research Strategies Appliedmentioning

confidence: 99%

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Mapping the Ethics of Learning Analytics in Higher Education: A Systematic Literature Review of Empirical Research

Pargman

McGrath

2021

JLA

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Application of learning analytics for sequential patterns detection associated with moments of distraction in students in e‐learning platforms

Rodríguez,

Riaño,

García

et al. 2023

Comp Applic In Engineering

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Owing to the positive impact of technology on education, in recent years, e‐learning platforms have increased in popularity and implementation. However, different authors agree that, despite the benefits of using these platforms, one of the main problems is the distraction during the interaction between the student and the E‐Learning platform, since the students visit web resources as social networks, which is not related to their learning process. to identify patterns associated with moments of distraction that students can present on these platforms. Following the above, this study aims to apply Learning Analytics using the Density‐based spatial clustering of applications with noise (DBSCAN) clustering technique and the PrefixSpan algorithm, to identify frequent sequential patterns of interaction, before a distraction event of the student from their interaction with a virtual learning platform. From this, clustering with a Silhouette coefficient close to 0.75 is obtained, presenting good separation, high cohesion, and compaction in the generated groups. Additionally, a slight characterization of each group is achieved based on the most frequent sequential patterns of interaction. Finally, in this study, a successful integration between DBSCAN and PrefixSpan is achieved, to identify sequential interaction patterns associated with moments of distraction in E‐Learning platforms, which generates a starting point for later studies to make the sequential recommendation of educational resources from these patterns.

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“We Know What You Were Doing”

Velander

Otero

Pargman

et al. 2021

Advances in Analytics for Learning and Teaching

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Technological barriers and incentives to learning analytics adoption in higher education: insights from users

Cited by 46 publications

References 23 publications

Mapping the Ethics of Learning Analytics in Higher Education: A Systematic Literature Review of Empirical Research

Mapping the Ethics of Learning Analytics in Higher Education: A Systematic Literature Review of Empirical Research

Application of learning analytics for sequential patterns detection associated with moments of distraction in students in e‐learning platforms

“We Know What You Were Doing”

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