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.     

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.     

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)。     

Assessing collaborative learning: big data,analytics and university futures

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.     

Beyond intelligent tutoring systems: Using computers as METAcognitive tools to enhance learning?

Framed by the existing theoretical andempirical research on cognitive and intelligenttutoring systems (ITSs), this commentaryexplores two areas not directly or extensivelyaddressed by Akhras and Self (this issue). Thefirst area focuses on the lack of conceptualclarity of the proposed constructivist stanceand its related constructs (e.g., affordances,situations). Specifically, it is argued that aclear conceptualization of the novelconstructivist stance needs to be delineated bythe authors before an evaluation of theirambitious proposal to model situationscomputationally in intelligent learningenvironments (ILEs) can be achieved. The secondarea of exploration deals with the similaritiesbetween the proposed stance and existingapproaches documented in the cognitive,educational computing, and AI in educationliterature. I believe that the authors are at acrossroads, and that their article presents aninitial conceptualization of an important issuerelated to a constructivist-based approach tothe computational modeling of situations inILEs. However, conceptual clarity isdefinitively required in order for theirapproach to be adequately evaluated and used toinform the design of ILEs. As such, I invitethe authors to re-conceptualize their frameworkby addressing how their constructivist stancecan be used to address a particular researchagenda on the use of computers as metacognitivetools to enhance learning.     

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.     

Supporting the less-adaptive student: the role of learning analytics,formative assessment and blended learning

Abstract

How can we best facilitate students most in need of learning support, entering a challenging quantitative methods module at the start of their bachelor programme? In this empirical study into blended learning and the role of assessment for and as learning, we investigate learning processes of students with different learning profiles. Specifically, we contrast learning episodes of two cluster analysis-based profiles, one profile more directed to deep learning and self-regulation, the other profile more directed toward stepwise learning and external regulation. In a programme based on problem-based learning, where students are supposedly being primarily self-directed, this first profile is regarded as being of an adaptive type, with the second profile less adaptive. Making use of a broad spectrum of learning and learner data, collected in the framework of a dispositional learning analytics application, we compare these profiles on learning dispositions, such as learning emotions, motivation and engagement, learning performance and trace variables collected from the digital learning environments. Outcomes suggest that the blended design of the module with the digital environments offering many opportunities for assessment of learning, for learning and as learning together with actionable learning feedback, is used more intensively by students of the less adaptive profile.     

Speaking the unspoken in learning analytics: troubling the defaults

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.     

Round or rectangular tables for collaborative problem solving? A multimodal learning analytics study

The current knowledge of the effects of the physical environment on learners’ behaviour in collaborative problem-solving tasks is underexplored. This paper aims to critically examine the potential of multimodal learning analytics, using new data sets, in studying how the shapes of shared tables affect the learners’ behaviour when collaborating in terms of patterns of participation and indicators related to physical social interactions. The research presented in this paper investigates this question considering the potential interplay with contextual aspects (level of education) and learning design decisions (group size). Three dependent variables (distance between students, range of movement and level of participation) are tested using quantitative and qualitative analyses of data collected using a motion capture system and video recordings. Results show that the use of round tables (vs rectangular tables) leads to higher levels of on-task participation in the case of elementary school students. For university students, different table shapes seem to have a limited impact on their levels of participation in collaborative problem solving. The analysis shows significant differences regarding the relationship between group size and the distance between students, but there is no substantial evidence that group size affects the level of participation. The findings support previous research highlighting the importance of studying the role of the physical environment as an element of learning design and the potential of multimodal learning analytics in approaching these studies.     

Being more human: rooting learning analytics through resistance and reconnection with the values of higher education

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.     

Supporting adaptive learning pathways through the use of learning analytics: developments,challenges and future opportunities

Learning Analytics (LA) and adaptive learning are inextricably linked since they both foster technology-supported learner-centred education. This study identifies developments focusing on their interplay and emphasises insufficiently investigated directions which display a higher innovation potential. Twenty-one peer-reviewed studies are identified via a systematic review and classified based on the dimensions of a proposed framework. The findings of the review suggest that interesting work has been carried out during the last years on the topic. We conclude with a need for more research on the topic in specific learning domains and settings. Recommendations include: a clear strategy for adaptation augmented by LA, the combination of on-task with pre-task measures, and the combination of system-controlled adaptation with user-controlled adaptation. Future trends include the emergence of constructivist-collaborative environments that provide insightful models of complex student behaviour.     

Students' personal networks in virtual and personal learning environments: a case study in higher education using learning analytics approach

The main objective of this paper is to analyse the effect of the affordances of a virtual learning environment and a personal learning environment (PLE) in the configuration of the students' personal networks in a higher education context. The results are discussed in light of the adaptation of the students to the learning network made up by two undergraduate, inter-university and online courses. Besides, we also examine the influence of this effect in the learning process. The findings reflect the effectiveness of a PLE for facilitating student participation and for assisting students in the creation of larger and more balanced personal networks with richer social capital. However, the findings do not provide evidences about a difference in the learning performance between the two environments. From a methodological point of view, this paper serves as an illustration of the analysis of personal networks on digital data collected from technology-enhanced learning environments.     

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.

     

Dialogue as a catalyst for teacher change: A conceptual analysis

Teacher–teacher dialogue is a central activity within many professional learning programmes. Understanding how and why dialogue works as an effective tool for teacher change is a question, however, that needs more careful probing in the extant literature. In this paper, I draw upon the philosophical theory of practical reason in order to show why and how teacher–teacher dialogue plays such a crucial role in the evolution of teacher practice. This conceptual analysis also builds our understanding as to the kinds of teacher–teacher dialogues that are more likely to be effective at catalysing teacher learning.