Social networks,communication styles,and learning performance in a CSCL community

The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning social networks, when they had work together on the design of aerospace systems using online collaboration tools. The results showed that both individual and structural factors (i.e., communication styles and a pre-existing friendship network) significantly affected the way the learners developed collaborative learning social networks. More specifically, learners who possessed high willingness to communicate (WTC) or occupied initially peripheral network positions were more likely to explore new network linkages. We also found that the resultant social network properties significantly influenced learners’ performance to the extent that central actors in the emergent collaborative social network tended to get higher final grades. The study suggests that communication and social networks should be central elements in a distributed learning environment. We also propose that the addition of personality theory (operationalized here as communication styles) to structural analysis (SNA) contributes to an enhanced picture of how distributed learners build their social and intellectual capital in the context of CSCL.     

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.     

Mapping the study of learning analytics in higher education

ABSTRACT

In recent years, the application of technological innovation in higher education has become more and more widely spread, and technological innovation has been improving the level of education. In the research of higher education with innovation technology, one of the main focuses is on the dynamic data which can lay a foundation for the analysis of educational activities by learning analytics. The dynamic data created by technological innovation will become the key basis for analytical research and development in higher education. The methods and analysis results of learning analytics will directly affect decision-making and strategy about higher education. In this paper, we use bibliometric and visualisation methods to review the literature, in order to highlight the development of learning analytics in higher education. Using bibliometric analysis, our study depicts the development process of the main methods used in learning analytics, and summarises the current situation in this field, which increases the level of understanding provided by those studies. Finally, we summarise the research hotspots and study trends, which will be useful for future study in this field.     

Participation in virtual academic communities of practice under the influence of technology acceptance and community factors. A learning analytics application

Participation in virtual communities of practice (vCoP) can be influenced at the same time by technology acceptance and by community factors. To overcome methodological issues connected with the analysis of these influences, learning analytics were applied. Based on a recent vCoP model, the collaborative dialogue comprising 4040 interventions in 1981 messages created by a vCoP located at a US American online university was automatically analyzed. The text-based asynchronous online discussions were scored using a cohesion-based participation and collaboration analysis. Additionally, a sample of N = 133 vCoP participants responded a technology acceptance survey. Thus, a combined research model including the vCoP model and an established technology acceptance model was verified. The results confirmed the vCoP model entirely, and the acceptance model only partially. As consequence for educational research, the CoP model was confirmed and extended to vCoP settings, while the acceptance model appears to need reconsideration. For academic practice, the study initiates the development of assessment tools fostering knowledge sharing through dialogue in vCoP. Also, it suggests how virtual classrooms can be extended to open spaces where value creation takes place through social learning. Learning analytics proved thus successful, provides information that impacts both theory and practice of technology-enhanced learning.     

Queer identity online: Informal learning and teaching experiences of LGBTQ individuals on social media

In this study, we explored how social media, particularly social networking sites, serve as informal learning environments for lesbian, gay, bisexual, transgender, questioning, and otherwise-identified (LGBTQ) individuals during formative stages of their evolving LGBTQ identity. We conducted semi-structured interviews (N = 33) probing LGBTQ individuals’ use of social media and identified three educational uses tied to online information seeking: traditional learning (e.g., information seeking about LGBTQ-related issues), social learning (e.g., observing role models or other LGBTQ individuals’ behavior and experiences), and experiential learning (e.g., experimenting with online dating sites and dating apps). These experiences were especially common during the coming out process. Participants also reported a fourth educational function, teaching (e.g., sharing information with others about their experiences as an LGBTQ individual). Teaching was more common among individuals who were out and those with less common identities (e.g., asexual and transgender). Several affordances of social media, including visibility, association, persistence, anonymity, and interactivity enabled these learning experiences.     

Social gaming is inSIDE: Impact of anonymity and group identity on performance in a team game-based learning environment

The present experiment aimed to determine how quiz performance in a team game-based learning environment can be predicted from the Social Identity model of Deindividuation Effects (SIDE). According to this model, anonymity influences social behavior by accentuating the salience of group identity and reducing interpersonal differences, leading to greater group identification and motivation to work for one's own group. As these effects could lead to higher cognitive performance, the goal of the present research was to extend predictions based on the SIDE model on performance in online game-based learning environments. After measuring their prior computing knowledge, 343 Master Degree students were placed in virtual teams on a trivial criterion to perform a series of online quizzes about computing and the Internet. An anonymous (or individuated) username was attributed to each team member to connect to the online learning environment, and information about comparison between teams was used to manipulate the degree of salience of group identity (high versus low). As predicted by the SIDE model, anonymity boosted performance when group identity was salient, but only for students with low prior knowledge. Unexpectedly, it was also found that anonymity boosted the performance of students with high prior knowledge when group identity was not salient. A similar pattern was found for perceived mastery of computing and the Internet. Theoretical and practical implications of the SIDE model are discussed, and specifically its application to social gaming to optimize online learning.     

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.     

Investigating factors that influence social presence and learning outcomes in distance higher education

There are many factors that influence distance learning especially in higher education where collaborative and communicative discourse is necessary for pursuing knowledge. Social presence, among other factors, is an important concept to be facilitated, developed and sustained in distance higher education as it promotes and supports discourse based learning. This study examines the relationship among demographic and other variables, social presence and learning satisfaction. Results showed demographic variables, such as gender, online learning experience and work status were not significant factors in terms of influencing on either social presence or learning satisfaction. While media integration and instructor’s quality teaching were significant predictors of both social presence and learning satisfaction, interactivity among participants was a predictor of social presence but not of learning satisfaction. Along with the study findings, some implications were discussed for online learning practitioners in higher education setting.     

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.     

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.     

Challenges and opportunities of multimodal data in human learning: The computer science students' perspective

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non-technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student-facing MLA innovations.     

Visual Analytics on Network Forgetting for Task-Incremental Learning

Task-incremental learning (Task-IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under-explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in-depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time.     

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.     

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.     

Business value of big data analytics: A systems-theoretic approach and empirical test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.     

Exploring collaborative learning effect in blended learning environments

The use of new technology encouraged exploration of the effectiveness and difference of collaborative learning in blended learning environments. This study investigated the social interactive network of students, level of knowledge building and perception level on usefulness in online and mobile collaborative learning environments in higher education. WeChat, which is a mobile synchronous communication tool, and modular object‐oriented dynamic learning environment (Moodle) were used as mobile and online collaborative learning settings. Seventy‐eight college students majoring in information engineering participated in the experiment. The following findings were revealed by combining methods of social network analysis, content analysis and questionnaire survey: (1) the collaborative social networks generated in this study showed that students had tighter interaction relationships in Moodle than in WeChat; (2) deeper level of knowledge building in collaboration and interaction through Moodle than WeChat was observed; and (3) Moodle got higher perception level than WeChat because of its usefulness for collaboration.     

Micro-persistence and difficulty in a game-based learning environment for computational thinking acquisition

Persistence has been identified as a crucial quality of learning. However, it is hard to attain in online game-based environments as the drive to progress in the game may influence the ability to achieve the learning goals. This study aimed to examine the associations between micro-persistence, that is, the tendency to complete an individual task successfully, and task difficulty while acquiring computational thinking (CT). We further explored whether contextual or personal attributes better explain micro-persistence. We analysed data of 111 school students who used the CodeMonkey platform. We took a learning analytics approach for analysing the platform's log files. We found that micro-persistence is associated with task difficulty and that students who demonstrated an aptitude to learn new material are motivated to achieve the best solution. We also found that contextual variables better-explained micro-persistence than personal attributes. Encouraging micro-persistence can improve CT acquisition and the learning processes involved.     

Applying learning analytics dashboards based on process-oriented feedback to improve students' learning effectiveness

With the development of a technology-supported environment, it is plausible to provide rich process-oriented feedback in a timely manner. In this paper, we developed a learning analytics dashboard (LAD) based on process-oriented feedback in iTutor to offer learners their final scores, sub-scale reports, and corresponding suggestions on further learning content. We adopted a quasi-experimental design to investigate the effectiveness of the report on students' learning. Ninety-four freshman from two classes participated in this research. The two classes were divided into the LAD group and the original analytics report (OAR) based on a product-oriented feedback group. Before the experiment, all the students took the prior knowledge assessment. After a semester's instruction, all the students took the post-test of the summative assessment. Results indicated that students in the LAD group experienced better learning effectiveness than students in the OAR group. LAD based on process-oriented feedback was also effective in improving the skill learning effectiveness of the students with low-level prior knowledge.     

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.     

Advanced machine learning approaches to personalise learning: learning analytics and decision making

The aim of the paper is to present methodology to personalise learning using learning analytics and to make further decisions on suitability, acceptance and use of personalised learning units. In the paper, first of all, related research review is presented. Further, an original methodology to personalise learning applying learning analytics in virtual learning environments and empirical research results are presented. Using this learning personalisation methodology, decision-making model and method are proposed to evaluate suitability, acceptance and use of personalised learning units. Personalised learning units evaluation methodology presented in the paper is based on (1) well-known principles of Multiple Criteria Decision Analysis for identifying evaluation criteria; (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model, and (3) probabilistic suitability indexes to identify learning components’ suitability to particular students’ needs according to their learning styles. In the paper, there are also examples of implementing the methodology using different weights of evaluation criteria. This methodology is applicable in real life situations where teachers have to help students to create and apply learning units that are most suitable for their needs and thus to improve education quality and efficiency.