Analytics-driven redesign of an instructional course

Molina-Carmona, Rafael; Villagrá-Arnedo, Carlos; Gallego-Durán, Francisco J.; Largo, Faraón Llorens

doi:10.1145/3144826.3145391

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…However, the majority of studies appealed to relevant concepts and approaches rather than established theoretical frameworks, approaching the issue of LA‐LD integration. These included the following examples: LA process model and stage‐based model of personal informatics systems (Kokoç & Altun, 2021), technology‐enhanced learning design patterns (Kaliisa et al, 2020), learning analytics implementation principles of coordination, comparison, and customization (Lancaster et al, 2020), ADDIE instructional design model (Molina‐Carmona et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

“…The actual improvements described in the papers related to: Improving course content and activities (‘creating several new drag‐and‐drop activities’, ‘adding better quality LaTex equations’, ‘chunked some of our Adaptive Tutorials into multiple separate learning modules’ (Ford et al, 2021, p. 4); ‘incorporates a weekly exploring ideas strand’ (Willans et al, 2019, p. 6). Providing support to students (‘improving the adaptive feedback’, ‘added general information screens at the beginning’ (Ford et al, 2021, p. 4); ‘additional “preparing for lectures” notes and activities are inserted’, ‘directs students to key sections of each text, gives them strategies’ (Willans et al, 2019, pp. 4–5); ‘helping students to identify pages that contain essential knowledge that must be thoroughly studied’ (Yang et al, 2021, p. 10). Adjusting the study load (‘structure of deadlines has been maintained’, ‘a new checkpoint has been added at week 10’ (Molina‐Carmona et al, 2017, p. 6); ‘posting materials well in advance’ (Rayyan et al, 2016, p. 190). Conducting additional monitoring (‘a special monitoring action was added’ (Molina‐Carmona et al, 2017, p. 6); ‘configure the monitoring process’ (Rodríguez‐Triana et al, 2015, p. 11). Enhancing the accessibility (‘enhancing the accessibility of the adaptive tutorials for mobile devices such as iPads’ (Ford et al, 2021, p. 4). …”

Section: Resultsmentioning

confidence: 99%

“…In addition, student engagement (Yang et al, 2021), cognitive engagement (de Leng & Pawelka, 2020), student retention (Rienties et al, 2016), student satisfaction (Rienties et al, 2016), academic advising and student self‐reflection (Ozdemir et al, 2019) were examined. Evaluation included the use of questionnaires, observations, semi‐structured interviews and focus groups (Rodríguez‐Triana et al, 2015), survey research (Tadesse & Davidsen, 2019), student feedback analysis (Knight et al, 2018), action research (Molina‐Carmona et al, 2017), tool trials (Kitto et al, 2017), user study (Albó et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

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Learning analytics driven improvements in learning design in higher education: A systematic literature review

Drugova,

Zhuravleva,

Zakharova

et al. 2023

Computer Assisted Learning

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BackgroundDriven by the ongoing need to provide high‐quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear.ObjectivesThis study is aimed to evaluate the maturity of research discussing LA‐driven LD improvements in higher education.MethodsThe systematic review analyses 49 empirical papers, assesses their quality and suggests further research directions. The review elaborates on methodological (research questions, strategy and methods, LA‐LD integration theoretical backgrounds) and substantial (LA‐driven LD improvements, types of data used, LA software development) features of the papers.Results and ConclusionsThe findings demonstrated the lack of theoretical alignment between LA and LD, with research tending towards user experience studies. The most frequently used research strategy was a case study; experiments were very rare. Researchers predominantly used parsing for collecting data and AI methods for analysing it; mostly used data types related to registering learners' engagement with learning activities as well as resources and tools provided in digital learning environments.TakeawaysThe research area discussing LA‐driven LD improvements still has a way to go before attaining the level of full maturity. Only a third of the papers reported actual LA‐driven LD improvements; moreover, only three papers measured their effectiveness. The presented LA software was mostly at the beta or implementation stages and did not assess the impact of using this software.

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