A bibliometric perspective of learning analytics research landscape

ABSTRACT

Learning analytics is an emerging field of research, motivated by the wide spectrum of the available educational information that can be analysed to provide a data-driven decision about various learning problems. This study intends to examine the research landscape of learning analytics to deliver a comprehensive understanding of the research activities in this multidisciplinary field, using scientific literature from the Scopus database. An array of state-of-the-art bibliometric indices is deployed on 2811 procured publication datasets: publication counts, citation counts, co-authorship patterns, citation networks and term co-occurrence. The results indicate that the field of learning analytics appears to have been instantiated around 2011; thus, before this time period no significant research activity can be observed. The temporal evolution indicates that the terms ‘students’, ‘teachers’, ‘higher education institutions’ and ‘learning process’ appear to be the major components of the field. More recent trends in the field are the tools that tap into Big Data analytics and data mining techniques for more rational data-driven decision-making services. A future direction research depicts a need to integrate learning analytics research with multidisciplinary smart education and smart library services. The vision towards smart city research requires a meta-level of smart learning analytics value integration and policy-making.     

学习分析技术在大数据环境中的应用

学习分析是大数据在教育应用中的焦点,本文对学习分析的核心环节进行技术剖析,梳理主要的学习分析工具,以实证研究的方式,从课程建设者、教学管理者和辅导教师这3种不同用户视角展示学习分析技术的应用过程。研究以某课程平台的学习行为数据作为研究样本,应用统计、可视化、聚类、关联规则等方法,采用Excel,SPSS,Weka等工具,分析课程模块访问频次,了解不同教学组对学生登录周数的影响,刻画学生的分类特征,发现隐含的内在规律。研究表明,学习分析技术充分发挥了教育大数据的价值,使数据成为教学干预、实施决策的重要依据。     

Applying social learning analytics to message boards in online distance learning: A case study

Social learning analytics introduces tools and methods that help improving the learning process by providing useful information about the actors and their activity in the learning system. This study examines the relation between SNA parameters and student outcomes, between network parameters and global course performance, and it shows how visualizations of social learning analytics can help observing the visible and invisible interactions occurring in online distance education.The findings from our empirical study show that future research should further investigate whether there are conditions under which social network parameters are reliable predictors of academic performance, but also advises against relying exclusively in social network parameters for predictive purposes. The findings also show that data visualization is a useful tool for social learning analytics, and how it may provide additional information about actors and their behaviors for decision making in online distance learning.     

Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning

Learning analytics is the analysis of electronic learning data which allows teachers, course designers and administrators of virtual learning environments to search for unobserved patterns and underlying information in learning processes. The main aim of learning analytics is to improve learning outcomes and the overall learning process in electronic learning virtual classrooms and computer-supported education. The most basic unit of learning data in virtual learning environments for learning analytics is the interaction, but there is no consensus yet on which interactions are relevant for effective learning. Drawing upon extant literature, this research defines three system-independent classifications of interactions and evaluates the relation of their components with academic performance across two different learning modalities: virtual learning environment (VLE) supported face-to-face (F2F) and online learning. In order to do so, we performed an empirical study with data from six online and two VLE-supported F2F courses. Data extraction and analysis required the development of an ad hoc tool based on the proposed interaction classification. The main finding from this research is that, for each classification, there is a relation between some type of interactions and academic performance in online courses, whereas this relation is non-significant in the case of VLE-supported F2F courses. Implications for theory and practice are discussed next.     

基于用户视角下学习分析技术的应用与挑战

In the big data environment, with the rapid development of education information technology, learning analytics has been a hot research topic in recent years. In order to provide references for the follow-up study, based on the relevant literatures, this paper expounds the concept and characteristics of the learning analytics, from the user point of view of learners, teachers and teaching managers, and discusses the application of learning analytics in network learning and the problems and challenges faced by them.     

From signals to knowledge: A conceptual model for multimodal learning analytics

Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is possible to capture observable events of the learning process such as learner's behaviour and the learning context. The learning process, however, consists also of latent attributes, such as the learner's cognitions or emotions. These attributes are unobservable to sensors and need to be elicited by human‐driven interpretations. We conducted a literature survey of experiments using multimodal data to frame the young research field of multimodal learning analytics. The survey explored the multimodal data used in related studies (the input space) and the learning theories selected (the hypothesis space). The survey led to the formulation of the Multimodal Learning Analytics Model whose main objectives are of (O1) mapping the use of multimodal data to enhance the feedback in a learning context; (O2) showing how to combine machine learning with multimodal data; and (O3) aligning the terminology used in the field of machine learning and learning science.     

城市道路交通数据可视分析综述

目的 交通是困扰现代大都市的世界性难题.近年来,可视分析技术在分析和利用交通大数据中扮演了越来越重要的角色,成为一项重要的智能交通技术.本文将全面回顾自信息可视化和可视分析兴起以来城市交通数据可视分析领域的研究现状.方法 从道路交通流量分析和其他交通问题分析两个方面,按照数据的类型及问题的分类探讨交通领域的可视化技术和可视分析系统,简单回顾近年来出现的研究新趋势.结果 早期研究注重对道路流量的可视化展示方案,主要方法有箭头图、马赛克图和轨迹墙等.随着可视分析手段的丰富,对城市道路交通流量的分析层次上升到交通事件层面,但是交通事件的定义仅局限于交通拥堵.应用可视分析的其他交通问题领域包括公共交通、交通事故和人群出行行为等.近年出现了挖掘和利用交通轨迹或交通事件的社会属性或称环境上下文信息的研究新趋势.结论 从对交通流量的可视化到交通事件的可视分析,从面向道路交通状况到与交通相关的其他社会性问题,从单纯反映路况的交通数据到富含社会性语义的多源数据,从传统的PC端可视化和交互范式到新型的可视化展示介质,交通数据可视化领域的研究在深度和广度上都得到大大拓展,未来该领域的研究趋势也体现于其中.     

Improving Cyber Security Awareness and Training Programs with Data Analytics

ABSTRACT

This article it is a modest attempt to explore the issues facing cyber security awareness and training programs and potential benefits of using learning analytics, an emerging field in data analytics, for combining existing data sources to provide additional value to these programs. This article was written under the assumption that awareness and training are valid preventive controls and therefore the pros and cons of implementing such programs are not being discussed.     

The impact of Social Multimedia Systems on cyberlearners

The phenomenon of social learning analytics presents a synergy between variety of disciplines, such as business intelligence, educational data mining, cyberlearning, and cyber infrastructure. The main contribution of this research is to combine two types of social learning analytics, social learning network analysis and social learning content analysis in studying the impact of the Social Multimedia Systems (SMSs) on cyberlearners. The research study provided in this paper is based on the survey data collected in spring 2011 at Western Kentucky University. The evidence obtained from the analysis shows that SMS impacts (a) the digital communication between faculty and students; (b) students’ success and grades; (c) the amount of materials covered and learned; (d) the effectiveness of studying; (e) the depth of learning; (f) the ability to focus on the most important learning objectives; (g) the degree of collaboration among students; and (h) the students’ motivation of studying.     

Exploring Students Engagement Towards the Learning Management System (LMS) Using Learning Analytics

Learning analytics is a rapidly evolving research discipline that uses the insights generated from data analysis to support learners as well as optimize both the learning process and environment. This paper studied students’ engagement level of the Learning Management System (LMS) via a learning analytics tool, student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review (SLR) was employed for the selection, sorting and exclusion of articles from diverse renowned sources. The findings show that most of the engagement in LMS are driven by educators. Additionally, we have discussed the factors in LMS, causes of low engagement and ways of increasing engagement factors via the Learning Analytics approach. Nevertheless, apart from recognizing the Learning Analytics approach as being a successful method and technique for analyzing the LMS data, this research further highlighted the possibility of merging the learning analytics technique with the LMS engagement in every institution as being a direction for future research.     

Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.     

Tracking precursors of learning analytics over serious game team performance ranking

ABSTRACT

The use of serious games to improve collaborative skill transfer and retention has received considerable attention from scholars, web marketing practitioners and business consultants. Team rankings and learning progress in game learning analytics, however, have yet to be empirically examined. Using fuzzy-set qualitative comparative analysis to study the performance of competing teams in a web marketing serious game (Simbound), we highlight a combination of causal conditions (engagement, reach and profitability) affecting team rankings. This paper proposes a conceptual architecture of the forces that influence learning progress within a collaborative learning environment. This learning environment is studied for web marketing boot camps powered by Simbound at three European universities: Grenoble Alpes University (France), University of Milano-Bicocca (Italy) and Dunarea de Jos University of Galati (Romania). Gaining knowledge of cases through game learning analytics is valuable for two reasons: It emphasises the instructor’s role in mobilising players’ engagement, and it tests variability across cases, offering precursors of team performance rankings. This approach to collective skill retention highlights the moderating factors of team performance rankings, whilst purposely calibrating a gameable learning environment. This paper enriches our knowledge of how active experimentation in learning analytics metrics can develop skills for real business competition.     

Viewing Visual Analytics as Model Building

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model‐building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal‐oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.     

Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning

Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates the use of diverse sensors, including computer vision, user‐generated content, and data from the learning objects (physical computing components), to record high‐fidelity synchronised multimodal recordings of small groups of learners interacting. We processed and extracted different aspects of the students' interactions to answer the following question: Which features of student group work are good predictors of team success in open‐ended tasks with physical computing? To answer this question, we have explored different supervised machine learning approaches (traditional and deep learning techniques) to analyse the data coming from multiple sources. The results illustrate that state‐of‐the‐art computational techniques can be used to generate insights into the "black box" of learning in students' project‐based activities. The features identified from the analysis show that distance between learners' hands and faces is a strong predictor of students' artefact quality, which can indicate the value of student collaboration. Our research shows that new and promising approaches such as neural networks, and more traditional regression approaches can both be used to classify multimodal learning analytics data, and both have advantages and disadvantages depending on the research questions and contexts being investigated. The work presented here is a significant contribution towards developing techniques to automatically identify the key aspects of students success in project‐based learning environments, and to ultimately help teachers provide appropriate and timely support to students in these fundamental aspects.     

Analytics of learning tactics and strategies in an online learnersourcing environment

Background

The use of crowdsourcing in a pedagogically supported form to partner with learners in developing novel content is emerging as a viable approach for engaging students in higher-order learning at scale. However, how students behave in this form of crowdsourcing, referred to as learnersourcing, is still insufficiently explored.

Objectives

To contribute to filling this gap, this study explores how students engage with learnersourcing tasks across a range of course and assessment designs.

Methods

We conducted an exploratory study on trace data of 1279 students across three courses, originating from the use of a learnersourcing environment under different assessment designs. We employed a new methodology from the learning analytics (LA) field that aims to represent students' behaviour through two theoretically-derived latent constructs: learning tactics and the learning strategies built upon them.

Results

The study's results demonstrate students use different tactics and strategies, highlight the association of learnersourcing contexts with the identified learning tactics and strategies, indicate a significant association between the strategies and performance and contribute to the employed method's generalisability by applying it to a new context.

Implications

This study provides an example of how learning analytics methods can be employed towards the development of effective learnersourcing systems and, more broadly, technological educational solutions that support learner-centred and data-driven learning at scale. Findings should inform best practices for integrating learnersourcing activities into course design and shed light on the relevance of tactics and strategies to support teachers in making informed pedagogical decisions.     

Towards an integrated learning analytics framework for quality perceptions in higher education: a 3-tier content,process, engagement model for key performance indicators

ABSTRACT

The integration of quality in Higher Education is multidimensional. Higher Education administration, programs, procedures and evaluation provide the context for the application and diffusion of quality metrics. Our research intends to provide a holistic discussion on Key Performance Indicators (KPIs) related to quality in Higher Education. The analysis of recent literature resulted in the construction of two research tools. The first one is related to a structured agenda for a qualitative interview targeted at Higher Education administrators. The second is related to a quantitative research model that analyses the relations of various quality factors. We provide a mapping of quality perceptions as discussed in previous work and we construct a theoretical model for the affordances of scholars to this perception. The research design includes interviews with academic administrators and teaching staff involved in the creation of academic programmes and courses. The main contribution is an analytic discussion of their perceptions about quality that updates significantly contemporary literature in interesting dimensions. Three-dimensional value space with twenty factors is presented. The outcomes of this research work are used as input for our quantitative study. In fact, a list of 20 quality factors is exploited in three main dimensions of learning analytics namely: content, process and engagement. Key Performance Indicators are highlighted for further investigation.     

Measuring the impact of technological scaffolding interventions on micro-level processes of self-regulated workplace learning

This paper reports on the findings of an exploratory study in which the effects of technological scaffolding interventions on micro-level processes of self-regulated learning in the workplace were investigated. Empirical research in the workplace has been much less represented than in formal education. Even less research is available that aimed to identify which technological scaffolding interventions, out of those available in a learning environment, had the highest influence on specific micro-level process of self-regulated learning. This paper reports on the findings of a case study conducted in the naturalistic settings of two organizations in Europe (N = 53) for the period of two months. Trace data about the events of engagement with the technological scaffolding interventions and micro-level processes of self-regulated learning were collected. Both a transition graph based analysis of the temporal dependencies of the collected events and multiple linear regression analyses showed that an intervention that promoted social awareness had consistently the highest effect on all the micro-level processes used in the study. This intervention was followed by the intervention that offered system-generated recommendations about learning paths, learning activities and knowledge assets to stimulate engagement into the micro-level processes within the forethought or preparatory phase of self-regulated learning. These findings suggest that both the social and organizational contexts should be taken into account when developing interventions aimed at supporting the forethought and engagement phases. Further discussion about research, methodological, and learning technology design implications is provided.     

PDP: Parallel Dynamic Programming

Deep reinforcement learning is a focus research area in artificial intelligence. The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods. The principle of adaptive dynamic programming (ADP) is first presented instead of direct dynamic programming (DP), and the inherent relationship between ADP and deep reinforcement learning is developed. Next, analytics intelligence, as the necessary requirement, for the real reinforcement learning, is discussed. Finally, the principle of the parallel dynamic programming, which integrates dynamic programming and analytics intelligence, is presented as the future computational intelligence.      

利用问题求解理论来研究交互式复杂信息的可视分析行为

面对大数据的挑战,力图将人的推理能力和计算系统的数据处理能力相结合的交 互式可视分析研究变得愈发重要。然而目前仍缺乏有效的认知理论来指导面向复杂信息的可视 分析系统的设计,诸如意义构建等现有的理论框架通常着眼于分析行为的外在特征,未能对此 类行为的内在认知机理进行深入研究。因此提出将问题求解作为一种理论框架来解释交互可视 分析行为的基本认知活动,并建议从非良构问题的角度来描述可视分析过程中用户所面临的主 要挑战,还从问题表征及问题求解策略等角度分析了可视分析系统对分析行为的影响。本研究 在理论上,将认知心理学领域的问题求解理论引入到交互可视分析行为的研究中,该方法对设 计面向复杂信息分析的其他类型交互系统也有启示作用;在实践层面上,从问题求解的支持角 度探索了可视分析系统的设计和评估问题。