Desperately Seeking the Impact of Learning Analytics in Education at Scale

Viberg, Olga; Grönlund, Åke

doi:10.1201/9781003194620-2

Cited by 15 publications

(12 citation statements)

References 22 publications

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“…The potential of learning analytics (LA) and the advantages that LA services can offer to higher education have been acknowledged by researchers and practitioners worldwide, but their adoption still remains low (Viberg & Grönlund, 2021;Tsai et al, 2020;Cuzmán-Valenzuela et al, 2021). LA is defined as "the measurement, collection, analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occurs" (Long & Siemens, 2011, p. 34).…”

Section: Introductionmentioning

confidence: 99%

Exploring students’ expectations of learning analytics: A person-centered approach

et al. 2022

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In order to successfully implement learning analytics (LA), we need a better understanding of student expectations of such services. Yet, there is still a limited body of research about students’ expectations across countries. Student expectations of LA have been predominantly examined from a view that perceives students as a group of individuals representing homogenous views. This study examines students’ ideal (i.e., representing their wanted outcomes) and predicted expectations (i.e., unveiling what they realistically expect the LA service is most likely to be) of LA by employing a person-centered approach that allows exploring the heterogeneity that may be found in student expectations. We collected data from 132 students in the setting of Swedish higher education by means of an online survey. Descriptive statistics and Latent Class Analysis (LCA) were used for the analysis. Our findings show that students’ ideal expectations of LA were considerably higher compared to their predicted expectations. The results of the LCA exhibit that the Swedish students’ expectations of LA were heterogeneous, both regarding their privacy concerns and their expectations of LA services. The findings of this study can be seen as a baseline of students’ expectations or a cross-sectional average, and be used to inform student-centered implementation of LA in higher education.

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Section: Introductionmentioning

confidence: 99%

Exploring students’ expectations of learning analytics: A person-centered approach

et al. 2022

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“…Furthermore, allowing students to have an active voice in determining what data are collected about themselves, how it is used and stored, who will have access to the data, and how student identities will be protected is important to boost students' perceptions of ability to control personal information (Roberts et al, 2016). Also, there is a need to involve students in the design and implementation of LA systems (Ifenthaler & Schumacher, 2016; Viberg & Grönlund, 2021), by undertaking participatory design approaches.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the efforts and the importance of enabling a responsible approach to LA (Viberg & Grönlund, 2021), the nature of students' privacy concerns in LA has not yet been explicitly explored and understood. To better address the intricacies of privacy within a particular context, it is crucial first to comprehend the nature of privacy concerns, including root causes and consequences from the data subject perspective for that specific context (Smith et al, 1996; Wu, 2014).…”

Section: Introductionmentioning

confidence: 99%

Students' privacy concerns in learning analytics: Model development

Mutimukwe

Viberg

Öberg

et al. 2022

Brit J Educational Tech

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Understanding students' privacy concerns is an essential first step toward effective privacy‐enhancing practices in learning analytics (LA). In this study, we develop and validate a model to explore the students' privacy concerns (SPICE) regarding LA practice in higher education. The SPICE model considers privacy concerns as a central construct between two antecedents—perceived privacy risk and perceived privacy control, and two outcomes—trusting beliefs and non‐self‐disclosure behaviours. To validate the model, data through an online survey were collected, and 132 students from three Swedish universities participated in the study. Partial least square results show that the model accounts for high variance in privacy concerns, trusting beliefs, and non‐self‐disclosure behaviours. They also illustrate that students' perceived privacy risk is a firm predictor of their privacy concerns. The students' privacy concerns and perceived privacy risk were found to affect their non‐self‐disclosure behaviours. Finally, the results show that the students' perceptions of privacy control and privacy risks determine their trusting beliefs. The study results contribute to understand the relationships between students' privacy concerns, trust and non‐self‐disclosure behaviours in the LA context. A set of relevant implications for LA systems' design and privacy‐enhancing practices' development in higher education is offered. What is already known about this topic Addressing students' privacy is critical for large‐scale learning analytics (LA) implementation. Understanding students' privacy concerns is an essential first step to developing effective privacy‐enhancing practices in LA. Several conceptual, not empirically validated frameworks focus on ethics and privacy in LA. What this paper adds The paper offers a validated model to explore the nature of students' privacy concerns in LA in higher education. It provides an enhanced theoretical understanding of the relationship between privacy concerns, trust and self‐disclosure behaviour in the LA context of higher education. It offers a set of relevant implications for LA researchers and practitioners. Implications for practice and/or policy Students' perceptions of privacy risks and privacy control are antecedents of students' privacy concerns, trust in the higher education institution and the willingness to share personal information. Enhancing students' perceptions of privacy control and reducing perceptions of privacy risks are essential for LA adoption and success. Contextual factors that may influence students' privacy concerns should be considered.

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“…To emphasize the fact that teacher inquiry does not always mean working around "problems" but could have an improvement goal in mind (Viberg & Gronlund, 2021), we did not start our model with "problem identification" but rather "purpose" (i.e., Lai & Schildkamp, 2013). We hope that this might draw more teachers towards analytics use.…”

Section: Iteration 1: Existing Teacher Inquiry Models Informing the Amtimentioning

confidence: 99%

“…Teachers' limited data use can be pinpointed to a lack of access to relevant data to satisfy their needs (Viberg & Gronlund, 2021) and its high workload compared to the expected gain (Hansen & Wasson, 2016). Still, not much research is available about this topic.…”

Section: Introductionmentioning

confidence: 99%

Towards data-informed teaching practice:

2022

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Data-informed decision making in teachers’ practice, now recommended by different teacher inquiry models and policy documents, implies deep practice change for many teachers. However, not much is known how teachers perceive the different steps that analytics-informed teacher inquiry into their own practice entails. This paper presents the results of a study into developing an Analytics Model for Teacher Inquiry (AMTI), which was then used to understand how teachers (N=10) construe the steps in the model and to explore the possible constraints as well as incentives for Teaching and Learning Analytics (TLA)-informed teacher practices. In the final iteration experts (N=7) and teacher-researchers (N=2) tested and evaluated the developed model. Their feedback was used to improve the model and provide example cases with insights into possible scenarios for TLA-informed analyses of teaching.

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Desperately Seeking the Impact of Learning Analytics in Education at Scale

Cited by 15 publications

References 22 publications

Exploring students’ expectations of learning analytics: A person-centered approach

Exploring students’ expectations of learning analytics: A person-centered approach

Students' privacy concerns in learning analytics: Model development

Towards data-informed teaching practice:

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