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.

     

大学生自主性学习量表编制方法的研究

鉴于国内迄今为止尚没有专门用于测量自主学习的工具,无法对学生的自主学习能力是否改善作出直接、准确的评价,影响实验结果的科学解释和说明。有鉴于此,本文试图首先编制一份大学生自主性学习量表,以此作为评估自主学习的工具,并为以后的实验研究做准备。本文把自主学习分为7个维度,每个维度又包括若干次级维度,由此编制出分量表和总表。实证研究部分的结果表明,大学生自主性学习量表具有较好的信度和效度,可以初步用于测评大学生的自主学习能力。     

Analytics for learning design: A layered framework and tools

The field of learning design studies how to support teachers in devising suitable activities for their students to learn. The field of learning analytics explores how data about students' interactions can be used to increase the understanding of learning experiences. Despite its clear synergy, there is only limited and fragmented work exploring the active role that data analytics can play in supporting design for learning. This paper builds on previous research to propose a framework (analytics layers for learning design) that articulates three layers of data analytics—learning analytics, design analytics and community analytics—to support informed decision-making in learning design. Additionally, a set of tools and experiences are described to illustrate how the different data analytics perspectives proposed by the framework can support learning design processes.     

What changes,and for whom? A study of the impact of learning analytics-based process feedback in a large course

Recent developments in educational technologies have provided a viable solution to the challenges associated with scaling personalised feedback to students. However, there is currently little empirical evidence about the impact such scaled feedback has on student learning progress and study behaviour. This paper presents the findings of a study that looked at the impact of a learning analytics (LA)-based feedback system on students' self-regulated learning and academic achievement in a large, first-year undergraduate course. Using the COPES model of self-regulated learning (SRL), we analysed the learning operations of students, by way of log data from the learning management system and e-book, as well as the products of SRL, namely, performance on course assessments, from three years of course offerings. The latest course offering involved an intervention condition that made use of an educational technology to provide LA-based process feedback. Propensity score matching was employed to match a control group to the student cohort enrolled in the latest course offering, creating two equal-sized groups of students who received the feedback (the experimental group) and those who did not (the control group). Growth mixture modelling and mixed between-within ANOVA were also employed to identify differences in the patterns of online self-regulated learning operations over the course of the semester. The results showed that the experimental group showed significantly different patterns in their learning operations and performed better in terms of final grades. Moreover, there was no difference in the effect of feedback on final grades among students with different prior academic achievement scores, indicating that the LA-based feedback deployed in this course is able to support students’ learning, regardless of prior academic standing.     

高中生自主学习状况的调查研究

该研究对526名高中生的自主学习状况进行了调查。结果表明,高中生自主学习的总体状况处于中等水平,自主学习各个方面的发展相对平衡,但对学习结果进行自我评价的自主性较差。高中学生的自主学习能力存在明显的学校差异和性别差异。高中生自主学习的能力呈年级下降趋势,而且在学习过程和学习环境的自主性方面存在显著的年级差异。     

Present Research on the Flipped Classroom and Potential Tools for the EFL Classroom

ABSTRACT

The flipped classroom can support the implementation of a communicative, student-centered learning environment in the English as a foreign language classroom. Unfortunately, there is little research which supports the incorporation of flipped learning in the English as a foreign language classroom. Numerous studies have focused on flipped learning and other subject areas, but more research needs to be done on the English as a foreign language classroom. With this in mind, there are many effective tools available to enable teachers to create a communicative, English as a foreign language learning environment. Even though technology is constantly changing, the tools presented here will give teachers an idea of how to incorporate and use technology when developing an active, communicative learning environment.     

个人英语词汇管理软件的功能设计

词汇是英语学习的基本内容。当前人们强调词汇学习中学习者自主学习意识、策略和习惯的培养。为探索如何利用信息技术促进英语词汇学习,研究者基于相关理论和研究,设计了个人英语词汇管理软件。文章描述了单词信息与单词关系管理、学习过程管理、网络资源利用以及跨平台支持等核心设计问题的决策依据和设计方案。作为"设计案例",文章可为相关学习工具的设计提供借鉴。     

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.     

Learning design in diverse institutional and cultural contexts: suggestions from a participatory workshop with higher education professionals in Africa

Learning design approaches, such as those adopted by the Open University, provide a set of tools and resources for purposefully-designing modules with a focus on student experiences. However, many of the current learning design strategies have been situated within specific institutions in Europe and North America. This means that there are several issues worth considering around if and how established learning design approaches make sense in diverse institutional and cultural contexts. To critically assess the relevance and appropriateness of learning design strategies in new contexts, this article describes an in-depth participatory workshop with 34 education professionals from five African countries. Altogether, 10 suggestions for learning design practices were derived from the consensus of workshop participants, which provide a foundation for the development of learning design practices moving forward.     

基于自律学习者培养的PBL教学探索

培养大学生自律学习能力、养成终身学习习惯是人才培养的终极目标。PBL教学倡导小组讨论与合作解决问题的学习,为大学生自律学习能力的培养提供了实现路径。阐述PBL教学设计的要素、各教学阶段设计要点及关键问题,为成功开展PBL教学提供有效指导。     

Degree pictures: Visualizing the university student journey

Abstract

The field of learning analytics is progressing at a rapid rate. New tools, with ever-increasing number of features and a plethora of datasets that are increasingly utilized demonstrate the evolution and multifaceted nature of the field. In particular, the depth and scope of insight that can be gleaned from analysing related datasets can have a significant, and positive, effect in educational practices. We introduce the concept of degree pictures, a symbolic overview of students’ achievement. Degree pictures are small visualizations that depict graphically 16 categories of overall student achievement, over the duration of a higher education course. They offer a quick summary of students’ achievement and are intended to initiate appropriate responses, such as teaching and pastoral interventions. This can address the subjective nature of assessment, by providing a method for educators to calibrate their own marking practices by showing an overview of any cohort. We present a prototype implementation of degree pictures, which was evaluated within our School of Computer Science, with favourable results.     

Small data,online learning and assessment practices in higher education: a case study of failure?

In this paper, we present an in-depth case study of a single student who failed an online module which formed part of a master’s programme in Professional Education and Leadership. We use this case study to examine assessment practices in higher education in the online environment. In taking this approach, we go against the current predilection for Big Data which has given rise to ‘learning analytics’, a data-intensive approach to monitoring learning. In particular, we draw attention to the model of the learner produced by learning analytics and to issues of ‘dataveillance’ in online learning. We also use the case to examine assessment in higher education more broadly, exploring the tensions between the requirements for certification and the need for learning. We conclude that assessment practices in higher education may have more to do with ‘quality assurance’ and regulatory frameworks than with ‘enhancing the student experience’ and inculcating the qualities that mark out higher education as an ethical project.     

自我调节策略在中学英语学习中的应用

随着经济全球化和中国加入世贸组织,英语作为一门国际通用语获得了越来越多的关注,同时有效的英语学习策略也成为英语学习者们的热点话题。中学阶段是英语学习者夯实基础和把握方向的关键时期。基于此,为中学英语学习者提供一种自我调节学习的有效学习模式,其中以社会认知观为理论基础,根据问卷调查结果,结合英语学习特征具体阐述自我调节学习策略的三个主要过程,以期进一步提高中学英语学习效果。     

Formative assessment strategies for students' conceptions—The potential of learning analytics

Formative assessment is considered to be helpful in students' learning support and teaching design. Following Aufschnaiter's and Alonzo's framework, formative assessment practices of teachers can be subdivided into three practices: eliciting evidence, interpreting evidence and responding. Since students' conceptions are judged to be important for meaningful learning across disciplines, teachers are required to assess their students' conceptions. The focus of this article lies on the discussion of learning analytics for supporting the assessment of students' conceptions in class. The existing and potential contributions of learning analytics are discussed related to the named formative assessment framework in order to enhance the teachers' options to consider individual students' conceptions. We refer to findings from biology and computer science education on existing assessment tools and identify limitations and potentials with respect to the assessment of students' conceptions.

Practitioner notes

What is already known about this topic

• Students' conceptions are considered to be important for learning processes, but interpreting evidence for learning with respect to students' conceptions is challenging for teachers.
• Assessment tools have been developed in different educational domains for teaching practice.
• Techniques from artificial intelligence and machine learning have been applied for automated assessment of specific aspects of learning.

What does the paper add

• Findings on existing assessment tools from two educational domains are summarised and limitations with respect to assessment of students' conceptions are identified.
• Relevent data that needs to be analysed for insights into students' conceptions is identified from an educational perspective.
• Potential contributions of learning analytics to support the challenging task to elicit students' conceptions are discussed.

Implications for practice and/or policy

• Learning analytics can enhance the eliciting of students' conceptions.
• Based on the analysis of existing works, further exploration and developments of analysis techniques for unstructured text and multimodal data are desirable to support the eliciting of students' conceptions.

     

Building upon synergies among self-regulated learning and formative assessment research and practice

ABSTRACT

The synergies that result from synthesising conceptual models and findings from self-regulated learning and formative assessment research are launching a new era of scholarship. They drive new questions about how to help students to internalise complimentary self-regulatory and assessment knowledge, skills, and dispositions as well as how to prepare teachers and education systems to prioritise and support this work. The articles in this special issue illustrate the power and importance of studying how self-regulation and classroom assessment emerge from social, contextual, historical, and cultural factors. Four themes emerged from the articles in this special issue, including new conceptualisations of self-regulated learning and formative assessment, insights regarding teachers’ dual role in enacting and promoting self-regulation, recognition of the critical role of students’ ability to productively use assessment information, and the need to instantiate self-regulated learning and assessment literacy as formal and valued curricular outcomes.     

语言自主性学习理论评述

人本主义心理学认为学习者具有发展潜能的能力和动力,研究者应促进其个性发展和潜能发挥;社会认知学派通过所构建的研究框架确定学习自主性程度,采用五个维度描述自主学习状态,认为自主学习能力习得是将外部学习技能内化为个人能力的过程;信息加工理论指出完整意义上的自主学习过程经历四个阶段,还构建了自主学习内在机制;元认知理论认为循序渐进的发展过程直接影响自主性学习能力的发展。对各理论学派教育哲学理念进行全面梳理,利于研究者借鉴其成果,从而提出本土化之理论。     

The Synergistic Effects of Self-Regulation Tools and the Flipped Classroom

ABSTRACT

Students at open-enrollment institutions enter college with a wide range of academic preparedness and are often required to take developmental classes to increase their academic skills to be successful in higher education. Further, few students possess self-regulated learning skills to aid in their learning. Researchers posited that academically at-risk students benefit from gains in self-regulated learning skills when a modeling and scaffolding approach is used to implement self-regulated learning tools. Self-regulated skills coupled with an active learning environment like the flipped classroom provide positive synergistic effects for academically at-risk students. This study compared several iterations of the flipped classroom in a general chemistry class at an open-enrollment college where high school class rank and mathematics placement level varied significantly. The results of the multiple regression analysis indicated that mathematics level and class rank were significant when predicting overall course grade regardless of the learning environment. The results of a paired-samples t test did not reveal a significant difference upon addition of note-taking and exam wrappers in a flipped classroom learning environment. However, students graduating high school in the top third, middle third, and bottom third of their graduating class increased their overall course grades in a flipped classroom using self-regulated tools by 7%, 3%, and 6%, respectively. To enhance the quantitative results, the author provides student comments on note-taking and the use of exam wrappers.     

A framework for classroom assessment,learning, and self-regulation

ABSTRACT

We present a conceptual framework that leverages synergies between classroom assessment (CA) practices and self-regulated learning (SRL) theory to support academic growth and instruction. We articulate the processes shared by CA and SRL, drawing on a model of SRL with three phases: forethought, performance, and self-reflection. We blend this SRL model with CA to create the CA:SRL framework in four stages: (1) pre-assessment, (2) the cycle of learning, doing, and assessing, (3) formal assessment, and (4) summarizing assessment evidence. We elucidate how SRL processes are involved at each stage and can be drawn on to support learning development and teacher understanding and co-regulation of learning. This framework is important in that it depicts how assessment and learning processes interact dynamically for both teachers and students in classrooms, and demonstrates that such interactions encompass the full breadth of purposes in CA, from planning through summation of evidence.     

Effective usage of learning analytics: what do practitioners want and where should distance learning institutions be going?

ABSTRACT

The implementation of learning analytics may empower distance learning institutions to provide real-time feedback to students and teachers. Given the leading role of the Open University UK (OU) in research and application of learning analytics, this study aims to share the lessons learned from the experiences of 42 participants from a range of faculty, academic and professional positions, and expertise with learning analytics. Furthermore, we explored where distance learning institutions should be going next in terms of learning analytics adoption. The findings from the Learning Analytics User Stories (LAUS) workshop indicated four key areas where more work is needed: communication, personalisation, integrated design, and development of an evidence-base. The workshop outputs signalled the aspiration for an integrated analytics system transcending the entire student experience, from initial student inquiry right through to qualification completion and into life-long learning. We hope that our study will spark discussion on whether (or not) distance learning institutions should pursue the dream of an integrated, personalised, and evidence-based learning analytics system that clearly communicates useful feedback to staff and students, or whether this will become an Orwellian nightmare.     

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.