Putting an ethical lens on learning analytics

West, Deborah; Huijser, Henk; Heath, David A.

doi:10.1007/s11423-016-9464-3

Cited by 55 publications

(84 citation statements)

References 29 publications

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“…Extending upon previous findings, academics in our focus groups expressed concern over the potential for the inappropriate use of learning analytics as a performance management tool (West et al, 2015). Whilst academics across all teaching levels were concerned about the potential misuse of learning analytics data for labelling or stereotyping of students (Corrin et al, 2013;West et al, 2016), staff members were also concerned about whether learning analytics truly captures the information required. Although there is promising research on how learning analytics improves retention and student grades (de Freitas et al, 2015), academics were concerned about whether the data obtained represents the best predictors of student success.…”

Section: Discussionmentioning

confidence: 73%

“…Whilst surveys suggest learning analytics are viewed as potentially beneficial to teaching practices academics also express skepticism over the utility of learning analytics (Corrin et al, 2013;Kregor et al, 2012;Miles, 2015). Some academics hold reservations concerning students' ability to interpret such feedback (Corrin et al, 2013;Miles, 2015), could the possible impact on students' self-esteem and capacity to learn and grow (Corrin et al, 2013;West, Huijser, & Heath, 2016) and the additional workload learning analytics would impose (Miles, 2015). As identified in the Technology Acceptance Model (Davis et al, 1989), if staff retain concerns about the usefulness of learning analytics then they are likely to have a reduced intention to use learning analytics, which may impede acceptance and integration of learning analytics in their teaching practices.…”

Section: When Academics Have Not Used Learning Analytics Research Inmentioning

confidence: 99%

“…(Willis et al 2016). The obligation to act centers on questions of if, when, how, and who engages with the student (Prinsloo & Slade, 2017;West et al, 2016;Willis et al, 2016). An important consideration is the inherent power imbalance between academics and students and the associated concept of fiduciary duty (Willis et al, 2016).…”

Section: When Academics Have Not Used Learning Analytics Research Inmentioning

confidence: 99%

“…With this imbalance in mind, concern has been expressed that learning analytics may be used punitively or to profile/label students (Corrin et Academics have identified transparency to students and staff as a key consideration for the implementation of learning analytics. Without this there is concern data may be used for purposes such as performance management (West et al, 2016). In a survey of academics, data openness and transparency were considered to be the second and third most impacted 'soft barriers' to the implementation of learning analytics (Drachsler & Greller, 2016).…”

Section: When Academics Have Not Used Learning Analytics Research Inmentioning

confidence: 99%

See 3 more Smart Citations

Are We on Our Way to Becoming a “Helicopter University”? Academics’ Views on Learning Analytics

et al. 2017

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Higher education institutions are developing the capacity for learning analytics. However, the technical development of learning analytics has far exceeded the consideration of ethical issues around learning analytics. We examined higher education academics' knowledge, attitudes, and concerns about the use of learning analytics though four focus groups (N = 35). Thematic analysis of the focus group transcripts identified five key themes. The first theme, 'Facilitating learning', represents academics' perceptions that, while currently unrealized, there could be several benefits to learning analytics that would help their students. Three themes; 'Where are the ethics?', 'What about the students!', and 'What about me!' represented academics' perceptions of how learning analytics could pose some considerable difficulties within a higher education context. A final theme 'Let's move forward together' reflected that despite some challenges and concerns about learning analytics, academics perceived scope for learning analytics to be beneficial if there is collaboration between academics, students, and the university. The findings highlight the need to include academics in the development of learning analytics policies and procedures to promote the suitability and widespread adoption of learning analytics in the higher education sector.

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Section: Discussionmentioning

confidence: 73%

Section: When Academics Have Not Used Learning Analytics Research Inmentioning

confidence: 99%

Section: When Academics Have Not Used Learning Analytics Research Inmentioning

confidence: 99%

Section: When Academics Have Not Used Learning Analytics Research Inmentioning

confidence: 99%

See 2 more Smart Citations

Are We on Our Way to Becoming a “Helicopter University”? Academics’ Views on Learning Analytics

et al. 2017

View full text Add to dashboard Cite

show abstract

“…They use static and dynamic information about learners and learning environments, assessing, eliciting, and analyzing it, for real-time modeling prediction, and optimization of learning processes, learning environments, and educational decision-making (Ifenthaler 2015). Current research on learning analytics focusses on technical issues and data processing (Berland et al 2014;Costa et al 2017), on data privacy (Drachsler and Greller 2016;Ifenthaler and Schumacher 2016;Rubel and Jones 2016;West et al 2016), on developing user systems (d'Aquin et al 2014), on relationships between learner characteristics and learning outcome (Ellis et al 2017;Gašević et al 2017;Liu et al 2017), or on specific applications for dashboards (Park and Jo 2015;Schumacher and Ifenthaler 2018;Verbert et al 2013). However, linking learning analytics with learning theories is still at an early stage (Marzouk et al 2016).…”

Section: Introductionmentioning

confidence: 99%

The importance of students’ motivational dispositions for designing learning analytics

Schumacher

2018

J Comput High Educ

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Depending on their motivational dispositions, students choose different learning strategies and vary in their persistence in reaching learning outcomes. As learning is more and more facilitated through technology, analytics approaches allow learning processes and environments to be analyzed and optimized. However, research on motivation and learning analytics is at an early stage. Thus, the purpose of this quantitative survey study is to investigate the relation between students' motivational dispositions and the support they perceive through learning analytics. Findings indicate that facets of students' goal orientations and academic self-concept impact students' expectations of the support from learning analytics. The findings emphasize the need to design highly personalized and adaptable learning analytics systems that consider students' dispositions and needs. The present study is a first attempt at linking empirical evidence, motivational theory, and learning analytics.

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Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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Before the emergence of computer‐based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014–2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state‐of‐the‐art and an acumens vision to inspire future labor. This article is categorized under: Application Areas > Education and Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

show abstract

Putting an ethical lens on learning analytics

Cited by 55 publications

References 29 publications

Are We on Our Way to Becoming a “Helicopter University”? Academics’ Views on Learning Analytics

Are We on Our Way to Becoming a “Helicopter University”? Academics’ Views on Learning Analytics

The importance of students’ motivational dispositions for designing learning analytics

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

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