Untangling connections between challenges in the adoption of learning analytics in higher education

Alzahrani, Asma Shannan; Tsai, Yi‐Shan; Iqbal, Sehrish; Marcos, Pedro Manuel Moreno; Scheffel, Maren; Drachsler, Hendrik; Kloos, Carlos Delgado; Aljohani, Naif Radi; Gašević, Dragan

doi:10.1007/s10639-022-11323-x

Cited by 11 publications

(5 citation statements)

References 44 publications

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“…For instance, these studies mention teachers' workload, trust and ownership by teachers, guidance, professional development, support, classroom orchestration, privacy concerns, technical infrastructure, data collection protocols and type of data analysis, all of them match with our 14 factors. In sum, as Alzahrani et al (2023) mention the main multidimensional factors are ethics and privacy, stakeholder buy-in and resources.…”

Section: Discussionmentioning

confidence: 99%

Adoption of learning analytics in higher education institutions: A systematic literature review

Márquez,

Henríquez,

Chevreux

et al. 2023

Brit J Educational Tech

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Learning analytics (LA) is an emerging area that has had extensive development in higher education in recent years, focused both on the learning process of students within subjects and on monitoring their trajectories in training programmes. However, most of the developments remain in the pilot phase without reaching institutional adoption. This paper reports the results of a systematic review of the literature carried out with the aim of identifying the factors that influence the adoption of LA, as well as the existing strategies that facilitate such adoption in higher education institutions. The results show that factors for LA adoption are situated in multiple dimensions, including stakeholders at different levels, institutional and pedagogical processes, technical limitations and ethical considerations. This work contributes with a consolidated list of 14 critical factors to be considered to effectively adopt LA tools, addressing planning, strong leadership, collaboration and prioritizing senior management commitment, goal setting, cross‐organizational design, educational process redesign, system integration and legacy system linkage. On the other hand, a variety of frameworks, models and approaches are distinguished in the literature to help the adoption of LA, however, none of them cover all the factors involved in such adoption. Therefore, we provide a compilation of strategies that have been used in the literature to reduce the gaps associated with the different factors described. Practitioner notesWhat is already known about this topic LA is a field of research that has had a lot of interest and a wide variety of tools have been developed. Still, it is criticized that they originate more from data availability than from the needs of students, teachers and decision makers. LA tools are mostly evaluated on their usability aspects and to a lesser extent on their usefulness to impact learning processes. The adoption processes of LA in higher education are oriented to specific aspects such as the relevance of the visualizations in the different contexts of application, but they do not address extensively other important aspects involved in such adoption. What this paper adds This article seeks to synthesize what has been investigated in the literature regarding the factors that affect the adoption of LA by higher education institutions. Describe the 14 critical factors identified from the literature to address adoption of multiple dimensions that include stakeholders at different levels, institutional contexts and ethical considerations. A compilation of strategies that have been used in the literature to reduce the gaps associated with the different factors are described, and the aspects or dimensions that are not addressed are highlighted. Implications for practice and/or policy The synthesis of LA adoption factors and the approach strategies present in the literature allow practitioners to focus their efforts on the key aspects to consider in their LA adoption processes. Future work could shift from a pure design perspective to strengthen an engineering perspective in the adoption practices. From the point of view of the policies associated with the adoption of LA in higher education institutions, it is concluded that it is necessary to approach the process from a comprehensive perspective considering all the dimensions involved.

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Section: Discussionmentioning

confidence: 99%

Adoption of learning analytics in higher education institutions: A systematic literature review

Márquez,

Henríquez,

Chevreux

et al. 2023

Brit J Educational Tech

View full text Add to dashboard Cite

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“…The largest collaborative network of authors focuses on the application of learning analytics to higher education, while issues of educational data ethics are considered one of the hindering factors. For example, ethical challenges that can be encountered in learning analytics applications and higher education contexts are explored (Alzahrani et al, 2022). The scholars who are often associated with Jones focused more on the ethical and privacy issues involved in learning analytics (Jones et al, 2020).…”

Section: Label (Llr)mentioning

confidence: 99%

The dilemma and countermeasures of educational data ethics in the age of intelligence

Guan

Feng

Islam

2023

Humanit Soc Sci Commun

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With the advent of the era of intelligent education, artificial intelligence and other technologies are being integrated into education, so that more educational data can be collected, processed, and analyzed. However, educational data ethics is an important factor that hinders the application of educational data. Thus, it is important to ensure privacy and security by reasonable use of educational data. As such, this research focuses on analysis of hot-points, trends in development and problems of educational data ethics in existing research using bibliometric analysis. Based on in-depth analysis, this study proposes targeted problem-solving strategies, and future developmental direction for learner-centered educational data ethics in the era of intelligent education. This study found three major problems in the educational data ethics: (a) violation of privacy during data collection, storage, and sharing, (b) the deprivation of the ability of educational subjects to make independent choices by prediction of educational data, (c) and the lack of “forgetting ability” based on data as the evaluation standard, which is one of the restrictions on the development of educational subjects. This research proposes that the corresponding problem-solving strategy in China should be learner-centered, combined with technologies such as blockchain, 5 G technology, and federated learning to form targeted solutions from different levels. Firstly, it is necessary to establish a standard system and related platforms of educational data from the macro level. Second, the research-practice dual channels should make efforts to build a new ecology of educational data. Third, schools and teachers must appropriately apply educational data in evaluation. Finally, this study provides direction and guidance for other countries or regions in researching educational data ethics.

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“…Alzahrani и др. [35], внедрение LA в высших учебных заведениях на сегодняшний день остается спорадическим и преимущественно небольшим по масштабу из-за ряда социально-технических проблем. Авторы попытались разобраться с этими проблемами, проведя свое исследование, которое было основано на серии интервью с руководителями высших учебных заведений, которые далее были тематически проанализированы с использованием возможностей эпистемического сетевого анализа.…”

Section: обзор литературыunclassified

On the principles of building a model of a specialist – a graduate of a pedagogical university

2023

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Introduction. Assessing students’ progress and predicting their future careers is paramount for any educational institution. The issue is particularly important for a teacher training institution, given the staff shortage in Russian schools and vocational education and training colleges. The educational process plays an important role in a graduate’s choice of future path, and hence, the problem of assessing the quality of this process arises as well.An option of solving this problem is to create a feedback system in the university, which allows evaluating the dynamics of students’ competency formation levels in the monitoring mode.Aim. The current research aims to develop and implement a multifactor competency model of a graduate of a pedagogical university, which can act as the basis for creating a system to monitor the quality of the educational process. The model takes into account psychological and socio-demographic factors, as well as students’ decisions in the field of a professional career.Methodology and research methods. The approbation of the proposed model was carried out on the data obtained as a result of the survey of students of Tomsk State Pedagogical University in 2021. The data were processed by conventional and multivariate statistical methods: factor and cluster analyses. The sample consisted of 189 students of all training courses of the Physics and Mathematics Faculty at Tomsk State Pedagogical University.Results. The analysis of the data obtained by using methods of multivariate statistics allowed the authors to obtain a variety of relationships between the factors taken into account. In particular, a pattern between the values of psychological scales, the level of competency formation and orientation of students in their choice of future profession was found.Scientific novelty. The block structure of the model makes it possible to analyse each block separately and together, selecting factors from different spaces, introducing new blocks or spaces of factors, oriented to the (possibly new) purpose of the study, without changing the others.Practical significance. The reasonableness of the obtained results of the study, the availability of a variety of algorithms for data interpretation indicate the real possibility of using in practice the proposed structure of the model, including the way of organising data as a monitoring system.

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Untangling connections between challenges in the adoption of learning analytics in higher education

Cited by 11 publications

References 44 publications

Adoption of learning analytics in higher education institutions: A systematic literature review

Adoption of learning analytics in higher education institutions: A systematic literature review

The dilemma and countermeasures of educational data ethics in the age of intelligence

On the principles of building a model of a specialist – a graduate of a pedagogical university

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