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1.
Abstract

In this article we investigate the effectiveness of learning analytics for identifying at-risk students in higher education institutions using data output from an in-situ learning analytics platform. Amongst other things, the platform generates ‘no-engagement’ alerts if students have not engaged with any of the data sources measured for 14 consecutive days. We tested the relationship between these alerts and student outcomes for two cohorts of first-year undergraduate students. We also compared the efficiency of using these alerts to identify students at risk of poorer outcomes with the efficiency of using demographic data, using widening participation status as a case study example. The no-engagement alerts were found to be more efficient at spotting students not progressing and not attaining than demographic data. In order to investigate the efficacy of learning analytics for addressing differential student outcomes for disadvantaged groups, the team also analysed the likelihood of students with widening participation status generating alerts compared with their non-widening participation counterparts. The odds of students with widening participation status generating an alert were on average 43% higher, demonstrating the potential of such a system to preferentially target support at disadvantaged groups without needing to target directly based on immutable factors such as their socio-economic background.  相似文献   

2.
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3.
Abstract

Assessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected and analysed, learning analytics allow for decisions to be made not only with regard to evaluating progress in achieving learning outcomes but also evaluative judgments about the quality of learning. Learning analytics fall in the nexus between assessment of and for learning. As such it has the potential to deliver value in the form of (1) understanding student learning, (2) analysing learning behaviour (looking to identify not only factors that may indicate risk of failing, but for opportunities to deepen learning), (3) predicting students-at-risk (or identifying where students have specific learning needs), and (4) prescribing elements to be included to ensure not only the effectiveness of teaching, but also of learning. Learning analytics have underlying default positions that may not only skew their impact but also impact negatively on students in realising their potential. We examine a selection of default positions and point to how these positions/assumptions may adversely affect students’ chances of success, deepening the understanding of learning.  相似文献   

4.
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.  相似文献   

5.
Abstract

The emergence of personalised data technologies such as learning analytics is framed as a solution to manage the needs of higher education student populations that are growing ever more diverse and larger in size. However, the current approach to learning analytics presents tensions between increasing student agency in making learning-related decisions and ‘datafying’ students in the process of collecting, analysing and interpreting data. This article presents a study that explores staff and student experience of agency, equity and transparency in existing data practices and expectations towards learning analytics in a UK university. The results show a number of intertwined factors that have contributed to the tensions between enhancing a learner’s control of their studies and, at the same time, diminishing their autonomy as an active agent in the process of learning analytics. This article argues that learner empowerment should not be automatically assumed to have taken place as part of the adoption of learning analytics. Instead, the interwoven power relationships in a complex educational system and the interactions between humans and machines need to be taken into consideration when presenting learning analytics as an equitable process to enhance student agency and educational equity.  相似文献   

6.
Abstract

Although it is frequently claimed that learning analytics can improve self-evaluation and self-regulated learning by students, most learning analytics tools appear to have been developed as a response to existing data rather than with a clear pedagogical model. As a result there is little evidence of impact on learning. Even fewer learning analytics tools seem to be informed by an understanding of the social context and social practices within which they would be used. As a result, there is very little evidence that learning analytics tools are actually impacting on practice. This paper draws on research in self-regulated learning and in the social practices of learning and assessment to clarify a series of design issues which should be considered by those seeking to develop learning analytics tools which are intended to improve student self-evaluation and self-regulation. It presents a case study of how these design issues influenced the development of a particular tool: the Learning Companion.  相似文献   

7.
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.
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8.
Abstract

The selection of career paths and making of academic choices is a difficult and often confusing task for young people. The impact on their lives, however, is enormous as it can determine entire future career possibilities. In India, a general remedy to this stress is that instead of choosing a field of study tailored to individual preferences and strengths, topics are chosen that align with the choices of the students’ families or their friends. This can have the effect of entrenching patterns of intergenerational inequity. The aim of this research is to give students greater access to the knowledge capital which will help them make better choices. This is achieved by engaging students in the career planning process, in order to convey information in a likeable and credible way. The COMPCAT (Competency and Career Assessment Tool) game engine combines the use of learning analytics and real time, interactive computer simulations designed to gain insights into the students’ engagement in the making of these complex decisions. This paper presents the conceptual architecture of the game and demonstrates its role in enhancing the learning effectiveness of the students.  相似文献   

9.
ABSTRACT

Universities are now compelled to attend to metrics that (re)shape our conceptualisation of the student experience. New technologies such as learning analytics (LA) promise the ability to target personalised support to profiled ‘at risk’ students through mapping large-scale historic student engagement data such as attendance, library use, and virtual learning environment activity as well as demographic information and typical student outcomes. Yet serious ethical and implementation issues remain. Data-driven labelling of students as ‘high risk’, ‘hard to reach’ or ‘vulnerable’ creates conflict between promoting personal growth and human flourishing and treating people merely as data points. This article argues that universities must resist the assumption that numbers and algorithms alone can solve the ‘problem’ of student retention and performance; rather, LA work must be underpinned by a reconnection with the agreed values relating to the purpose of higher education, including democratic engagement, recognition of diverse and individual experience, and processes of becoming. Such a reconnection, this article contends, is possible when LA work is designed and implemented in genuine collaboration and partnership with students.  相似文献   

10.
ABSTRACT

In this article, we examine the sociopolitical implications of AI technologies as they are integrated into writing instruction and assessment. Drawing from new materialist and Black feminist thought, we consider how learning analytics platforms for writing are animated by and through entanglements of algorithmic reasoning, state standards and assessments, embodied literacy practices, and sociopolitical relations. We do a close reading of research and development documents associated with Essay Helper, a machine learning platform that provides formative feedback on student writing based on standards-aligned rubrics and training data. In particular, we consider the performative acts of the algorithm in the Essay Helper platform – both in the ways that reconstitutes material-discursive relations of difference, and its implications for transactions of teaching and learning. We argue that, through these processes, the algorithms function as racializing assemblages, and conclude by suggesting pathways toward alternative futures that reconfigure the sociopolitical relations the platform inherits.  相似文献   

11.
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.  相似文献   

12.
ABSTRACT

Despite widespread enthusiasm, evidence of the effectiveness of learning analytics remains mixed. One possible explanation for this is that insufficient attention has been paid to the contexts in which it is introduced. We report here on a small-scale study into the prior use of data and communications technologies by tutors, who comprise a key user group in The Open University’s tuition model. Tutors interviewed reported using a complex set of data sources and information tools, and creating local/personal tools and methods for keeping track of students and their interactions with them.  相似文献   

13.
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)。  相似文献   

14.
Abstract

In this study, we present a case study involving two self-service dashboards providing feedback on learning and study skills and on academic achievement. These dashboards were offered to first-year university students in several study programmes in Flanders, Belgium. Data for this study were collected using usage tracking (N?=?2875) and a survey taken at the beginning of the second year before (N?=?484) and after (N?=?538) the introduction of the dashboards. We found that early dashboard usage is related to academic achievement later in the academic year and that students’ review of the feedback received in the first year improved. Although these results are modest in comparison to how high the bar is sometimes set for learning analytics applications, we argue that low-cost deployments of self-service dashboards are an interesting approach to start building experience with similar tools and to start paving the way for future developments.  相似文献   

15.
This mixed-method study focuses on online learning analytics, a research area of importance. Several important student attributes and their online activities are examined to identify what seems to work best to predict higher grades. The purpose is to explore the relationships between student grade and key learning engagement factors using a large sample from an online undergraduate business course at an accredited American university (n = 228). Recent studies have discounted the ability to predict student learning outcomes from big data analytics but a few significant indicators have been found by some researchers. Current studies tend to use quantitative factors in learning analytics to forecast outcomes. This study extends that work by testing the common quantitative predictors of learning outcome, but qualitative data is also examined to triangulate the evidence. Pre and post testing of information technology understanding is done at the beginning of the course. First quantitative data is collected, and depending on the hypothesis test results, qualitative data is collected and analyzed with text analytics to uncover patterns. Moodle engagement analytics indicators are tested as predictors in the model. Data is also taken from the Moodle system logs. Qualitative data is collected from student reflection essays. The result was a significant General Linear Model with four online interaction predictors that captured 77.5 % of grade variance in an undergraduate business course.  相似文献   

16.
Assessment in higher education has focused on the performance of individual students. This focus has been a practical as well as an epistemic one: methods of assessment are constrained by the technology of the day, and in the past they required the completion by individuals under controlled conditions of set-piece academic exercises. Recent advances in learning analytics, drawing upon vast sets of digitally stored student activity data, open new practical and epistemic possibilities for assessment, and carry the potential to transform higher education. It is becoming practicable to assess the individual and collective performance of team members working on complex projects that closely simulate the professional contexts that graduates will encounter. In addition to academic knowledge, this authentic assessment can include a diverse range of personal qualities and dispositions that are key to the computer-supported cooperative working of professionals in the knowledge economy. This paper explores the implications of such opportunities for the purpose and practices of assessment in higher education, as universities adapt their institutional missions to address twenty-first century needs. The paper concludes with a strong recommendation for university leaders to deploy analytics to support and evaluate the collaborative learning of students working in realistic contexts.  相似文献   

17.
Background: Recent effectiveness studies have investigated the relationship between two dimensions of effectiveness – namely, quality and equity. Specifically, the question of whether effective schools can also reduce the initial differences in student outcomes attributed to student background factors has been examined. In this context, the Dynamic Approach to School Improvement (DASI) makes use of theory and the research findings of effectiveness studies to try to improve school effectiveness in terms of quality and equity.

Purpose: This study aimed to examine whether the implementation of DASI in primary schools in socially disadvantaged areas in four European countries (Cyprus, England, Greece and Ireland) was able to promote student learning outcomes in mathematics and to reduce the impact of student background factors on student achievement in mathematics.

Design and methods: A sample of 72 primary schools across the four countries was randomly split into experimental and control groups. At the beginning and at the end of the school year, mathematics tests were administered to all students of Grades 4–6 (n = 5560; student ages 9–12 years). The experimental group made use of DASI. Within-country multilevel regression analyses were conducted to evaluate the impact of the intervention and search for interaction effects between the use of DASI and student background factors on final achievement.

Results: In each country, the experimental group achieved better results in mathematics than the control group. At the beginning of the intervention, the achievement gap based on socio-economic status (SES) was equally large in the experimental and the control groups. Only in the experimental group did the achievement gap based on SES become smaller. However, DASI was not found to have an effect on equity when the equity dimension was examined by focusing on the achievement gap based on either gender or ethnicity.

Conclusions: Implications of findings are drawn and the importance of measuring equity in terms of student achievement gaps based on different background factors, rather than only on SES, is emphasised. We propose the evaluation of the impact of interventions on promoting equity by the use of various criteria.  相似文献   

18.
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.  相似文献   

19.
ABSTRACT

Background and Context: In spite of the decades spent developing software visualization (SV), doubts still remain regarding their effectiveness. Furthermore, student engagement plays an important role in improving SV effectiveness as it is correlated with many positive academic outcomes. It has been shown that the existing SV has failed to engage students effectively.

Objective: Therefore, there is a need to understand the theories behind SV design from the engagement perspective to produce a roadmap for future tool construction. The aim of this study was to identify the theories have been used in literature to explain or construct student engage- ment with SV in computer science courses for novices.

Method: We performed a systematic literature review that identified a total of 58 articles published between 2011 and 2017, which were then selected for the study. However, among them, only 18 articles had discussed their theoretical framework.

Findings: The results of this study show a richness in the theoretical framework obtained from different disciplines, however, constructivism is still dominant in the computing education research (CER) domain. It is evidently clear from the findings that the theories generated from the CER domain are needed to enhance the effectiveness of SV.

Implications: As a result of this review, we suggest several design principles and engagement attributes to be considered while creating an engaging SV.  相似文献   

20.
Abstract

Researchers are becoming increasingly interested in the use of transformational leadership theory in higher education teaching (often referred to as transformational instructor-leadership). Much of this body of research investigates a direct association between transformational instructor-leadership and student outcomes. In the present study, we take a step further by investigating (a) student engagement as a mechanism in the relationship between transformational instructor-leadership and students’ academic performance and (b) structural distance as a moderator of the relationship between transformational instructor-leadership and student engagement. Using a sample of 183 students across the UK, the findings supported student engagement as a full mediator, but did not support structural distance as a moderator. This study contributes to theory by (a) showing a key underlying process through which transformational instructor-leadership is related to students’ academic performance and (b) empirically examining all three dimensions of student engagement. Limitations, suggestions for future research and practical implications are discussed.  相似文献   

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