How learning disposition data can help us translating learning feedback from a learning analytics application into actionable learning interventions, is the main focus of this empirical study. It ...extends previous work (Tempelaar, Rienties, & Giesbers, 2015), where the focus was on deriving timely prediction models in a data rich context, encompassing trace data from learning management systems, formative assessment data, e-tutorial trace data as well as learning dispositions. In this same educational context, the current study investigates how the application of cluster analysis based on e-tutorial trace data allows student profiling into different at-risk groups, and how these at-risk groups can be characterized with the help of learning disposition data. It is our conjecture that establishing a chain of antecedent-consequence relationships starting from learning disposition, through student activity in e-tutorials and formative assessment performance, to course performance, adds a crucial dimension to current learning analytics studies: that of profiling students with descriptors that easily lend themselves to the design of educational interventions.
•Formative assessment data have high predictive power in generating learning feedback.•Learning disposition data are most actionable: triggering educational interventions.•Dispositional LA is instrumental in chaining dispositions, traces, performance.•Student profiling based on traces allows characterization in terms of dispositions.
For decades, self-report measures based on questionnaires have been widely used in educational research to study implicit and complex constructs such as motivation, emotion, cognitive and ...metacognitive learning strategies. However, the existence of potential biases in such self-report instruments might cast doubts on the validity of the measured constructs. The emergence of trace data from digital learning environments has sparked a controversial debate on how we measure learning. On the one hand, trace data might be perceived as "objective" measures that are independent of any biases. On the other hand, there is mixed evidence of how trace data are compatible with existing learning constructs, which have traditionally been measured with self-reports. This study investigates the strengths and weaknesses of different types of data when designing predictive models of academic performance based on computer-generated trace data and survey data. We investigate two types of bias in self-report surveys: response styles (i.e., a tendency to use the rating scale in a certain systematic way that is unrelated to the content of the items) and overconfidence (i.e., the differences in predicted performance based on surveys' responses and a prior knowledge test). We found that the response style bias accounts for a modest to a substantial amount of variation in the outcomes of the several self-report instruments, as well as in the course performance data. It is only the trace data, notably that of process type, that stand out in being independent of these response style patterns. The effect of overconfidence bias is limited. Given that empirical models in education typically aim to explain the outcomes of learning processes or the relationships between antecedents of these learning outcomes, our analyses suggest that the bias present in surveys adds predictive power in the explanation of performance data and other questionnaire data.
By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described as ...being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data, necessitating further research in this direction. The aim of this study is to evaluate whether providing teachers in a distance learning higher education institution with PLA data predicts students' performance and empowers teachers to identify and assist students at risk. Using principles of Technology Acceptance and Academic Resistance models, a university-wide, multi-methods study with 59 teachers, nine courses, and 1325 students revealed that teachers can positively affect students' performance when engaged with PLA. Follow-up semi-structured interviews illuminated teachers' actual uses of the predictive data and revealed its impact on teaching practices and intervention strategies to support students at risk.
Flipped classroom (FC) approaches have gotten substantial attention in the last decade because they have a potential to stimulate student engagement as well as active and collaborative learning. The ...FC is generally defined as a strategy that flips the traditional education setting, i.e., the information transmission component of a traditional face-to-face lecture is moved out of class time. The FC relies on technology and is therefore suitable for online or blended learning, which were predominant forms of learning during the COVID-19 pandemic (March 2020–July 2021). In this paper we present a systematic literature review (SLR) of studies that covered online FC approaches in higher education during the pandemic. We analyzed 205 publications in total and 18 in detail. Our research questions were related to the main findings about the success of implementation of online FC and recommendations for future research. The findings indicated that those who had used FC approaches in face-to-face or blended learning environments more successfully continued to use them in online environments than those who had not used it before. The SLR opened possible questions for future research, such as the effectiveness of the FC for different courses and contexts, the cognitive and emotional aspects of student engagement, and students’ data protection. It pointed to the need to examine different aspects of online delivery of the FC more comprehensively, and with more research rigor.
Open World Learning Rienties, Bart; Hampel, Regine; Scanlon, Eileen ...
2022, 20220125, 2022-01-25, Volume:
1
eBook
Open access
This book provides state-of-the-art contemporary research insights into key applications and processes in open world learning. Open world learning seeks to understand access to education, structures, ...and the presence of dialogue and support systems. It explores how the application of open world and educational technologies can be used to create opportunities for open and high-quality education. Presenting ground-breaking research from an award winning Leverhulme doctoral training programme, the book provides several integrated and cohesive perspectives of the affordances and limitations of open world learning. The chapters feature a wide range of open world learning topics, ranging from theoretical and methodological discussions to empirical demonstrations of how open world learning can be effectively implemented, evaluated, and used to inform theory and practice. The book brings together a range of innovative uses of technology and practice in open world learning from 387,134 learners and educators learning and working in 136 unique learning contexts across the globe and considers the enablers and disablers of openness in learning, ethical and privacy implications, and how open world learning can be used to foster inclusive approaches to learning across educational sectors, disciplines and countries. The book is unique in exploring the complex, contradictory and multi-disciplinary nature of open world learning at an international level and will be of great interest to academics, researchers, professionals, and policy makers in the field of education technology, e-learning and digital education.
How successful online learners are in achieving their goals varies along geo-cultural and socioeconomic dimensions, as well as with learning design features. Despite diverse enrollments, most online ...courses adopt a one-size-fits-all design that presents the same learning activities to all learners. We studied how learning design can be adapted to improve learner persistence rate. We leveraged data from ten FutureLearn MOOCs (n = 49,582) to examine how variation in course activities, such as articles, videos, discussions and quizzes predicted learner persistence. We then assessed the heterogeneity in these associations by learners' geo-cultural and socioeconomic context. Our findings suggest that certain types of learning activities (e.g., discussion) facilitate progress for learners in one context (e.g., Anglo-Saxon), while inhibiting progress in another (e.g., South Asia). This research contributes new insights into the role of cultural variation in learning design preferences and can inform ongoing efforts to create online learning environments that are effective for learners from diverse backgrounds.
•There is no ideal combination of coherent learning activities that work for ALL learners.•Certain learning activity types facilitate progress for learners in one context while inhibiting another.•Preference for video over text was strong in collectivists, particularly those from the South Asian region.•Making MOOC a social learning space supported western learners from affluent economies.•Overall data analysis can mask geo-cultural or socioeconomic heterogeneity in the link between learning design and outcome.
Precision education requires two equally important conditions: accurate predictions of academic performance based on early observations of the learning process and the availability of relevant ...educational intervention options. The field of learning analytics (LA) has made important contributions to the realisation of the first condition, especially in the context of blended learning and online learning. Prediction models that use data from institutional information systems and logs of learning management systems have gained a good reputation in predicting underperformance and dropout risk. However, less progress is made in resolving the second condition: applying LA generated feedback to design educational interventions. In our contribution, we make a plea for applying dispositional learning analytics (DLA) to make LA precise and actionable. DLA combines learning data, as in LA, with learners' disposition data measured through self-report surveys. The advantage of DLA is twofold: first, it improves the accuracy of prediction, specifically early in the module, when limited LMS trace data are available. Second, the main benefit of DLA is in the design of effective interventions: interventions that focus on addressing individual learning dispositions that are less developed but important for being successful in the module. We provide an empirical analysis of DLA in an introductory mathematics module, demonstrating the important role that a broad range of learning dispositions can play in realising precision education.
Learning analytics dashboards (LADs) can provide learners with insights about their study progress through visualisations of the learner and learning data. Despite their potential usefulness to ...support learning, very few studies on LADs have considered learners’ needs and have engaged learners in the process of design and evaluation. Aligning with that, there is a limited understanding of what specific student cohorts, in particular distance and online learners, may seek from LADs to effectively support their studies. In this study, we present findings from 21 interviews with undergraduate distance learners, mainly high performers, that aimed to capture student perceptions about the usefulness of specific LAD features and the factors that explain these perceptions. Our findings revealed that amongst the LAD features favoured by students was the potential to receive study recommendations, whereas comparison with peers was amongst the least favoured elements, unless informed by qualitative information. Factors including information trust, attitudes, age, performance and academic self-confidence were found to explain these perceptions.
Pedagogically informed designs of learning are increasingly of interest to researchers in blended and online learning, as learning design is shown to have an impact on student behaviour and outcomes. ...Although learning design is widely studied, often these studies are individual courses or programmes and few empirical studies have connected learning designs of a substantial number of courses with learning behaviour. In this study we linked 151 modules and 111.256 students with students' behaviour (<400 million minutes of online behaviour), satisfaction and performance at the Open University UK using multiple regression models. Our findings strongly indicate the importance of learning design in predicting and understanding Virtual Learning Environment behaviour and performance of students in blended and online environments. In line with proponents of social learning theories, our primary predictor for academic retention was the time learners spent on communication activities, controlling for various institutional and disciplinary factors. Where possible, appropriate and well designed communication tasks that align with the learning objectives of the course may be a way forward to enhance academic retention.
•Pedagogically informed learning designs (LD) are increasingly of interest.•Few empirical studies have connected LD with behaviour, satisfaction and retention.•Using regression analyses we linked LDs of 151 modules and 111 K students.•LD has strong impact on behaviour, satisfaction, and performance.•Primary predictor for academic retention was communication activities.
This review aims to provide a concise overview of four distinct research fields: Artificial Intelligence and EDucation (AIED), Computer-Supported Collaborative Learning (CSCL), Educational Data ...Mining (EDM), and Learning Analytics (LA). While all four fields are focused on understanding learning and teaching using technology, each field has a relatively unique or common perspective on which theoretical frameworks, methods, and ontologies might be appropriate. In this review we argue that researchers should be encouraged to cross the boundaries of their respective field and work together to address the complex challenges in education.
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