Ethical and privacy principles for learning analytics

The massive adoption of technology in learning processes comes with an equally large capacity to track learners. Learning analytics aims at using the collected information to understand and improve the quality of a learning experience. The privacy and ethical issues that emerge in this context are tightly interconnected with other aspects such as trust, accountability and transparency. In this paper, a set of principles is identified to narrow the scope of the discussion and point to pragmatic approaches to help design and research learning experiences where important ethical and privacy issues are considered.     

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.

     

The social character of knowing and learning: implications of cultural psychology for educational technology

Abstract

A cultural-psychological view of knowledge and learning is presented. Its concerns are defined by comparative discussion of other theoretical traditions in psychology. The cultural view frames intelligent action as something that is mediated. This renders knowledge as participatory, distributed, and socially guided. It is argued that adoption of this perspective has implications for the support of learning and the design of resources, such as those associated with educational technology. It is suggested that a number of innovations of computer use within education implicitly endorse this cultural view of knowing. However, the cultural-psychological emphasis on social aspects of learning urges more careful protection of some educational practices from unplanned consequences of re-mediations with information and communications technology – particularly as these may arise within networked learning. Four traditional arenas for educational practice are analysed in order to illustrate the subtle nature of such social grounding     

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.

     

Learning design and learning analytics in mobile and ubiquitous learning: A systematic review

Mobile and Ubiquitous Learning (m/u-learning) are finding an increasing adoption in education. They are often distinguished by hybrid learning environments that encompass elements of formal and informal learning, in activities that happen in distributed settings (indoors and outdoors), across physical and virtual spaces. Despite their purported benefits, these environments imply additional complexity in the design, monitoring and evaluation of learning activities. The research literature on learning design (LD) and learning analytics (LA) has started to deal with these issues. This paper presents a systematic literature review of LD and LA, in m/u-learning. Apart from providing an overview of the current research in the field, this review elicits elements of common ground between both communities, as shown by the similar learning contexts and complementary research contributions, and based on the research gaps, proposes to: address m/u-learning beyond higher education settings, reinforce the connection between physical and virtual learning spaces, and more systematically align LD and LA processes.     

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.

     

The implications of the differences between design research and instructional systems design for educational technology researchers and practitioners

Design research (DR) has been an emerging research paradigm in the field of educational technology as well as in education generally for two decades. Educational design research integrates design and research into a socially responsible approach to inquiry related to learning and teaching. Given its still relative novelty, design research requires further discussion regarding what it is and how it can be effectively executed. Instructional Systems Design (ISD) is one of the major activities carried out by educational technologists. Both ISD and design research deal with the enactment of design to improve educational practice. This paper describes the differences and similarities between these two activities and addresses the implications of these differences and similarities for educational technology researchers and practitioners.     

The role of design ethics in maintaining students' privacy: A call to action to learning designers in higher education

Maintaining students' privacy in higher education, an integral aspect of learning design and technology integration, is not only a matter of policy and law but also a matter of design ethics. Similar to faculty educators, learning designers in higher education play a vital role in maintaining students' privacy by designing learning experiences that rely on online technology integration. Like other professional designers, they need to care for the humans they design for by not producing designs that infringe on their privacy, thus, not causing harm. Recognizing that widely used instructional design models are silent on the topic and do not address ethical considerations such as privacy, we focus this paper on how design ethics can be leveraged by learning designers in higher education in a practical manner, illustrated through authentic examples. We highlight where the ethical responsibility of learning designers comes into the foreground when maintaining students' privacy and well-being, especially in online settings. We outline an existing ethical decision-making framework and show how learning designers can use it as a call to action to protect the students they design for, strengthening their ethical design capacity.

Practitioner notes

What is already known about this topic

• Existing codes of ethical standards from well-known learning design organizations call upon learning designers to protect students' privacy without clear guidance on how to do so.
• Design ethics within learning design is often discussed in abstract ways with principles that are difficult to apply.
• Most, if not all, design models that learning design professionals have learned are either silent on design ethics and/or do not consider ethics as a valid dimension, thus, making design ethics mostly excluded from learning design graduate programs.
• Practical means for engaging in ethical design practice are scarce in the field.

What this paper adds

• A call for learning designers in higher education to maintain and protect students' privacy and well-being, strengthening their ethical design capacity.
• A demonstration of how to use a practical ethical decision-making framework as a designerly tool in designing for learning to maintain and protect students' privacy and well-being.
• Authentic examples—in the form of vignettes—of ethical dilemmas/issues that learning designers in higher education could face, focused on students' privacy.
• Methods—using a practical ethical decision-making framework—for learning design professionals in higher education, grounded in the philosophy of designers as the guarantors of designs, to be employed to detect situations where students' privacy and best interests are at risk.
• A demonstration of how learning designers could make stellar design decisions in service to the students they design for and not to the priorities of other design stakeholders.

Implications for practice and/or policy

• Higher education programs/institutions that prepare/employ learning designers ought to treat the topics of the designer's responsibility and design ethics more explicitly and practically as one of the means to maintain and protect students' privacy, in addition to law and policies.
• Learning designers in higher education ought to hold a powerful position in their professional practice to maintain and protect students' privacy and well-being, as an important aspect of their ethical design responsibilities.
• Learning designers in higher education ought to adopt a design thinking mindset in order to protect students' privacy by (1) challenging ideas and assumptions regarding technology integration in general and (2) detecting what is known in User Experience (UX) design as “dark patterns” in online course design.

     

Appropriate educational technology: Does “appropriateness” have implications for the theoretical framework of educational technology?

It is argued here that educational technology can be inappropriate in developing countries due to dependence on the practices, rather than the principles, of educational technology. Practices have, inevitably, a cultural dimension, and it is proposed that the solution to the problem is the clearer identification of principles and the encouragement of educational technologists to work from principles rather than from practices.     

基于混合学习的教育技术公共课的教学设计

分析了教育技术公共课的现状,提出混合学习能解决当前存在的问题,可以较好地实现该课程的教学目标,并详细地从学习者分析、环境分析、教学目标分析、教学策略和资源的设计、学习评价的设计5个方面阐述了如何在该课程中进行混合学习的设计。     

论教育科学研究的伦理问题--兼论协调市场经济、伦理道德和法律规范的关系

通过对文献资料的分析,明确了教育科学研究中的伦理道德原则,进而阐述了其具体表现形式.在此基础上,论述当代教育科学研究应注意协调市场经济、伦理道德和法律规范的制约关系.     

A large-scale test of the relationship between procrastination and performance using learning analytics

Abstract

Many studies have found a relationship between students’ self-reported procrastination and their grades. Few studies have used learning analytic data as a behavioural measure of procrastination in order to predict performance, and there is no systematic research on how this relationship may differ across assessments or disciplines. In this study we analyse nine years’ worth of institutional electronic submission records, a total of 73,608 assignment submissions, to examine the relationship between submission time and grades across assignments, students, courses, and disciplines in higher education. A significant negative relationship was found overall, with students who submitted closer to the deadline obtaining lower grades, however the size of the relationship was negligible, accounting for less than 1% of the variance in grades. The relationship varied significantly depending on student, assignment, course and discipline.