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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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.
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BFBNIB, DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NMLJ, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ, ZRSKP
Educators need to change their practice to adapt to a shifting educational context. By visualising learning design decisions, this article highlights the need to capture educators' “tacit” knowledge ...relating to course material, activity types and workload, through employing learning analytics methods in order to analyse the learning designs of courses taken by 60,000+ students, common pedagogical patterns are identified. When analysing 157 learning designs using a taxonomy of seven different learning activities, we found that the majority of educators used two types of learning activities most widely, namely assimilative activities (reading, watching videos and listening to audio) and assessment activities. Surprisingly, educators do not choose different activity types based upon function (eg, replace one type of student‐activating activity by another), but patterns can be seen where educators combine assimilative, productive and assessment activities or assimilative, finding and handling information and communication tasks. While educators rely heavily on assimilative and assessment activities, no positive correlation was found between any of the seven learning design activity types and student outcomes. Our initial findings suggest that student outcomes are negatively correlated with a high proportion of assimilative activities. Further studies are needed to establish whether particular learning design decisions are related to student outcomes and whether these findings can be replicated in different research settings.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
Research has shown online learners’ performance to have a strong association with their demographic characteristics, such as regional belonging, socio-economic standing, education level, age, gender, ...and disability status. Despite a growing number of studies exploring factors for successful online learning outcomes, most researchers have utilised one or a combination of very few learner characteristics. Moreover, a limited number of studies scrutinised the impact of individual characteristics on learning outcomes as learners progress in a course. The current research aims to explore the dynamic impact of demographic characteristics on academic outcomes in the online learning environment. We investigated and compared the dynamic influence of six demographic characteristics on online learning outcomes using a sample of 8581 UK based learners across four Open University online courses from four different disciplines. We found region, neighborhood poverty level, and prior education respectively, to be strong predictors of overall learning outcomes. However, at a fine-grain level, such influence varied temporally as the course progressed, as well as between different courses. To conclude with, we discussed the implications for institutional support on adopting a tailored approach towards a more personalised student support system.
•In Decision trees, some predictive variables were more contributing than others.•Region, multiple deprivation, and education mostly contributed in tree induction.•Linkage between learning outcome and gender or disability was trivial.•Precision and recall for each class calculated separately (accuracy upto 83.14%).•Evidently, predictive models were biased towards majority class (Pass in this case).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Most research related to learning in groups focuses on the unit of the group and/or group members. However, students may benefit from crossing the boundaries of their own group, as students in ...different groups may provide access to new, nonredundant knowledge and opportunities for learning. Whether boundary crossing between groups is beneficial for learning and academic performance has received limited conceptual and empirical attention. Using social network analysis and structural equation modeling, we contrasted pre/post network developments among 693 students (132 groups) across 4 modules at a UK business school. We examined whether it is better for students to invest in social relations in groups to learn and enhance academic performance or to (continue to) invest in social relations outside groups. Our findings indicated that students seemed to learn more from learning relations outside their group than from their own group members. Students with more intergroup relative to intragroup learning relations performed better on module assessments and throughout the academic year than students with more intragroup learning relations. Boundary crossing and intergroup learning deserves more empirical attention and experimentation on how to balance boundary crossing and effective group learning strategies.
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BFBNIB, NUK, PILJ, SAZU, UL, UM, UPUK
•Formative assessment data have high predictive power in generating learning feedback.•Track data from e-tutorial systems are second-best predictors for timely feedback.•Predictive power of LMS data ...falls short in LA applications with rich data sources.•Learning dispositions take a unique position being complementary to all other data.•Combination of several data sources in LA is key to get timely, predictive feedback.
Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and teachers. Track data from learning management systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In a large introductory quantitative methods module, 922 students were enrolled in a module based on the principles of blended learning, combining face-to-face problem-based learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance of and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
With the increased affordances of synchronous communication tools, more opportunities for online learning to resemble face‐to‐face settings have recently become available. However, synchronous ...communication does not afford as much time for reflection as asynchronous communication. Therefore, a combination of synchronous and asynchronous communication in e‐learning would seem desirable to optimally support learner engagement and the quality of student learning. It is still an open question though, how to best design online learning with a blend of synchronous and asynchronous communication opportunities over time. Few studies have investigated the relationship between learners' actual use of synchronous and asynchronous communication over time. Therefore, this study addressed that relationship in an online course (N = 110), taking into account student motivation, and employing a dynamic inter‐temporal perspective. In line with our assumptions, we found some support for the expected association between autonomous motivation and engagement in asynchronous and synchronous communication, be it restricted primarily to the first course period. Also, positive relations between engagement in synchronous and asynchronous communication were found, with the strongest influence from using asynchronous to synchronous communication. This study adds to the knowledge base needed to develop guidelines on how synchronous communication can be combined with asynchronous learning.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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