Students' privacy concerns in learning analytics: Model development

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.

Practitioner notes

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.

     

Understanding privacy and data protection issues in learning analytics using a systematic review

The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.

Practitioner notes

What is already known about this topic

• Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics.
• Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions.

What this study contributes

• Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle.
• Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views.
• Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics.

Implications for practice and/or policy

• Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics.
• Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders.
• Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.

     

Multimodal learning analytics for game-based learning

A distinctive feature of game-based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor-based technologies such as facial expression analysis and gaze tracking have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics informed by multimodal data captured during students’ interactions with game-based learning environments hold significant promise for developing a deeper understanding of game-based learning, designing game-based learning environments to detect maladaptive behaviors and informing adaptive scaffolding to support individualized learning. This paper introduces a multimodal learning analytics approach that incorporates student gameplay, eye tracking and facial expression data to predict student posttest performance and interest after interacting with a game-based learning environment, Crystal Island . We investigated the degree to which separate and combined modalities (ie, gameplay, facial expressions of emotions and eye gaze) captured from students (n = 65) were predictive of student posttest performance and interest after interacting with Crystal Island . Results indicate that when predicting student posttest performance and interest, models utilizing multimodal data either perform equally well or outperform models utilizing unimodal data. We discuss the synergistic effects of combining modalities for predicting both student interest and posttest performance. The findings suggest that multimodal learning analytics can accurately predict students’ posttest performance and interest during game-based learning and hold significant potential for guiding real-time adaptive scaffolding.     

Ethical issues in learning analytics: a review of the field

Learning analytics (LA) collects, analyses, and reports big data about learners to optimise learning. LA ethics is an interdisciplinary field of study that addresses moral, legal, and social issues; therefore, institutions are responsible for implementing frameworks that integrate these topics. Many of the ethical issues raised apply equally to educational data sets of any size. However, in this study, we focus on big data that increases the scale and granularity of data gathered. The purpose of this study is twofold: (a) to critically review the published (2011–2018) scientific literature on LA ethics issues and (b) to identify current trends and answer research questions in the field. This study’s research questions are as follows: what is essential in LA ethics for key educational stakeholders, and what should a proposed checklist for LA ethics include for specific educational stakeholders? After systematically searching online bibliographic databases, journals, and conferences, a literature review identified 53 articles from a sample of 562. The selected articles, based on critical and qualitative content analysis, were exhaustively analysed. The findings demonstrate the shortage of empirical evidence-based guidelines on LA ethics and highlight the need to establish codes of practices to monitor and evaluate LA ethics policies. Finally, this work proposes a useful checklist as an instructional design model for scholars, policymakers, and instructional designers, so that trusted partners may use LA responsibly to improve teaching and learning.

     

An overview of learning analytics

Learning analytics, the analysis and representation of data about learners in order to improve learning, is a new lens through which teachers can understand education. It is rooted in the dramatic increase in the quantity of data about learners and linked to management approaches that focus on quantitative metrics, which are sometimes antithetical to an educational sense of teaching. However, learning analytics offers new routes for teachers to understand their students and, hence, to make effective use of their limited resources. This paper explores these issues and describes a series of examples of learning analytics to illustrate the potential. It argues that teachers can and should engage with learning analytics as a way of influencing the metrics agenda towards richer conceptions of learning and to improve their teaching.     

Digital analytics in professional work and learning

In a wide range of fields, professional practice is being transformed by the increasing influence of digital analytics: the massive volumes of big data, and software algorithms that are collecting, comparing and calculating that data to make predictions and even decisions. Researchers in a number of social sciences have been calling attention to the far-reaching and accelerating consequences of these forces, claiming that many professionals, researchers, policy-makers and the public are just beginning to realise the enormous potentials and challenges these analytics are producing. Yet, outside of particular areas of research and practice, such as learning analytics, there has been little discussion of this to date in the broader education literature. This article aims to set out some key issues particularly relevant to the understandings of professional practice, knowledge and learning posed by the linkages of big data and software code. It begins by outlining definitions, forms and examples of these analytics, their potentialities and some of the hidden impact, and then presents issues for researchers and educators. It seeks to contribute to and extend debates taking place in certain quarters to a broader professional education and work audience.     

The role of learning theory in multimodal learning analytics

This study presents the outcomes of a semi-systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35 MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory-driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory-driven MMLA research and how this acceleration can extend or even create new theoretical knowledge.

Practitioner notes

What is already known about this topic

• Multimodal learning analytics (MMLA) is an emerging field of research with inherent connections to advanced computational analyses of social phenomena.
• MMLA can help us monitor learning activity at the micro-level and model cognitive, affective and social factors associated with learning using data from both physical and digital spaces.
• MMLA provide new opportunities to support students' learning.

What this paper adds

• Some MMLA works use theory, but, overall, the role of theory is currently limited.
• The three theories dominating MMLA research are embodied cognition, control–value theory of achievement emotions and cognitive load theory.
• Most of the theory-driven MMLA papers use theory ‘as is’ and do not consider the analytical and synthetic role of theory or aim to contribute to it.

Implications for practice and/or policy

• If the ultimate goal of MMLA, and AI in Education in general, research is to understand and support human learning, these studies should be expected to align their findings (or not) with established relevant theories.
• MMLA research is mature enough to contribute to learning theory, and more research should aim to do so.
• MMLA researchers and practitioners, including technology designers, developers, educators and policy-makers, can use this review as an overview of the current state of theory-driven MMLA.

     

Coordinating learning analytics policymaking and implementation at scale

Many Latin-American institutions recognise the potential of learning analytics (LA). However, the number of actual LA implementations at scale remains limited, notwithstanding considerable effort made to formulate guidelines and frameworks to support the LA policy development. Guidance on how to coordinate the interaction between the LA policymaking and implementation is mostly missing, leaving a difficult challenge up to practitioners. In this study we propose a coordination model to support future LA initiatives at scale. We explore the problem by comparing two cases in Belgium and Ecuador. Following up we use the LA implementation timeline as a driver for planning the interaction between the policymaking and implementation. We continue by testing an application of the model with LA experts predominantly from Latin-American institutions, asking them to map low-level items of the SHEILA policy framework to four implementation phases. The results of this mapping support that LA policy building can be spread over time, that it can coincide with LA implementation at scale, and that both efforts can be coordinated. It is hoped that this study will provide additional guidance for future Latin-American and other LA initiatives.     

Exploring autonomous learning capacity from a self-regulated learning perspective using learning analytics

Practising self-regulated learning (SRL) has been proposed to develop learning autonomy. However, there is lack of empirical evidence on how SRL strategies affect autonomous learning capacity. This study attempts to bridge that gap by utilizing the learners’ trace data for measuring the learners’ autonomous interactions, and investigates the effects of four SRL strategies on learners’ autonomous choices. The goal is to explain how the employed SRL strategies impact autonomous control (in terms of frequencies of self-enforced decisions, as well as time-spent on decision making). The results from an exploratory study with undergraduate learners (N = 113) shown that goal-setting and time-management have strong positive effects on autonomous control, effort-regulation moderately positively affects learners’ autonomy, while help-seeking has a strong negative effect. These findings provide empirical evidence and contribute to clarifying the role of each one of the SRL strategies in the development of autonomous learning capacity, from a learning analytics perspective. Limitations and potential implications for research and practice are also discussed.     

Data-driven problem based learning: enhancing problem based learning with learning analytics

Educational technology research and development - Problem based learning (PBL) supports the development of transversal skills and could underpin the training of a workforce competent to withstand...     

生态系统观视角下的学习分析

学习分析自从2011年出现以来,不管是作为一个研究重点还是实践领域,它一直在发展,从某种程度上讲已经成熟了。学习分析不但在增进我们对学生坚持学习和顺利完成学业的了解以及提高我们教学策略的效果等方面有巨大潜能,它还能帮助学生在更加知情的情况下做出选择。然而,学习分析在多大程度上影响学生学习?它在什么条件下能够充分发挥其潜能?这些问题引起一些关注。我们在这篇概念性文章中提出从生态系统观的角度理解学习分析,或是把它视为某一个生态系统的一部分,或是把它当成一个生态系统,这个系统由各种人为和非人为因素(行动者)组成,包含一系列相互交叉、常常互相依存且又是彼此一部分的变量。鉴于学习分析有提高学习效果的潜能,我们基于学习的社会批判视角提出学习分析的生态系统观。我们从机构和机构以外社会层面的微观、中观和宏观因素出发对学习分析进行阐述。学习分析的生态系统观不认为学生对自己的学习可以免责,而是更加细致入微地了解促成(或妨碍)学习发生的因素(行动者)。