Student profiling in a dispositional learning analytics application using formative assessment

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 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

A multi-modal study into students’ timing and learning regulation: time is ticking

Purpose This empirical study aims to demonstrate how the combination of trace data derived from technology-enhanced learning environments and self-response survey data can contribute to the investigation of self-regulated learning processes. Design/methodology/approach Using a showcase based on 1,027 students’ learning in a blended introductory quantitative course, the authors analysed the learning regulation and especially the timing of learning by trace data. Next, the authors connected these learning patterns with self-reports based on multiple contemporary social-cognitive theories. Findings The authors found that several behavioural facets of maladaptive learning orientations, such as lack of regulation, self-sabotage or disengagement negatively impacted the amount of practising, as well as timely practising. On the adaptive side of learning dispositions, the picture was less clear. Where some adaptive dispositions, such as the willingness to invest efforts in learning and self-perceived planning skills, positively impacted learning regulation and timing of learning, other dispositions such as valuing school or academic buoyancy lacked the expected positive effects. Research limitations/implications Due to the blended design, there is a strong asymmetry between what one can observe on learning in both modes. Practical implications This study demonstrates that in a blended setup, one needs to distinguish the grand effect on learning from the partial effect on learning in the digital mode: the most adaptive students might be less dependent for their learning on the use of the digital learning mode. Originality/value The paper presents an application of embodied motivation in the context of blended learning

Upper-division Student Understanding of Coulomb's Law: Difficulties with Continuous Charge Distributions

Utilizing the integral expression of Coulomb's Law to determine the electric potential from a continuous charge distribution is a canonical exercise in Electricity and Magnetism (E&M). In this study, we use both think-aloud interviews and responses to traditional exam questions to investigate student difficulties with this topic at the upper-division level. Leveraging a theoretical framework for the use of mathematics in physics, we discuss how students activate, construct, execute and reflect on the integral form of Coulomb's Law when solving problems with continuous charge distributions. We present evidence that junior-level E&M students have difficulty mapping physical systems onto the mathematical expression for the Coulomb potential. Common challenges include difficulty expressing the difference vector in appropriate coordinates as well as determining expressions for the differential charge element and limits of integration for a specific charge distribution. We discuss possible implications of these findings for future research directions and instructional strategies.Comment: 5 pages, 1 figure, 2 tables, accepted to 2012 PERC Proceeding

Using resource graphs to represent conceptual change

We introduce resource graphs, a representation of linked ideas used when reasoning about specific contexts in physics. Our model is consistent with previous descriptions of resources and coordination classes. It can represent mesoscopic scales that are neither knowledge-in-pieces or large-scale concepts. We use resource graphs to describe several forms of conceptual change: incremental, cascade, wholesale, and dual construction. For each, we give evidence from the physics education research literature to show examples of each form of conceptual change. Where possible, we compare our representation to models used by other researchers. Building on our representation, we introduce a new form of conceptual change, differentiation, and suggest several experimental studies that would help understand the differences between reform-based curricula.Comment: 27 pages, 14 figures, no tables. Submitted for publication to the Physical Review Special Topics Physics Education Research on March 8, 200

Understanding of differential equations in a highly heterogeneous student group

Differential equations (DEs) are an important mathematical concept for a wide variety of disciplines in engineering. Hence, students need to develop a good understanding of the basic concepts of DEs. However, they encounter many difficulties when studying DEs and often exclusively focus on procedural knowledge. This study therefore investigates the difficulties concerning DEs encountered by engineering students at a university of applied sciences in Germany. In contrast to previous studies on this topic our investigation differs in two aspects. First, the group of first-year engineering students at this university is highly heterogeneous; e.g. while some begin their studies immediately after secondary school, others have completed vocational training and joined the workforce for some time. Second, the engineering study programs considered here provide for only two semesters of mathematics and do not include specific courses on (ordinary) differential equations. The subject of DEs is dealt with in a three- to four-week period at the end of the second semester. We conducted think-aloud interviews lasting about 45 min with 9 students after completion of the relevant course. We found that the main difficulties students experience are connected to: substantial lack of prior knowledge, attempting (sometimes unsuccessfully) to apply memorized procedures, and a failure to understand both the difference between a DE and a function and what a solution to a DE is. The results shall be used to design three to four collaborative-group worksheets that build on students’ ways of thinking and aim at improving students’ conceptual understanding

Individual differences in the preference for worked examples: lessons from an application of dispositional learning analytics

Worked-examples have been established as an effective instructional format in problem-solving practices. However, less is known about variations in the use of worked examples across individuals at different stages in their learning process in student-centred learning contexts. This study investigates different profiles of students’ learning behaviours based on clustering learning dispositions, prior knowledge, and the choice of feedback strategies in a naturalistic setting. The study was conducted on 1,072 students over an eight-week long introductory mathematics course in a blended instructional format. While practising exercises in a digital learning environment, students can opt for tutored problem-solving, untutored problem-solving, or call worked examples. The results indicated six distinct profiles of learners regarding their feedback preferences in different learning phases. Finally, we investigated antecedents and consequences of these profiles and investigated the adequacy of used feedback strategies concerning ‘help-abuse’. This research indicates that the use of instructional scaffolds as worked-examples or hints and the efficiency of that use differs from student to student, making the attempt to find patterns at an overall level a hazardous endeavour

Adding dispositions to create pedagogy-based Learning Analytics

This empirical study aims to demonstrate how Dispositional Learning Analytics (DLA) can provide a strong connection between Learning Analytics (LA) and pedagogy. Where LA based models typically do well in predicting course performance or student drop-out, they lack actionable data in order to easily connect model predictions with educational interventions. Using a showcase based on learning processes of 1080 students in a blended introductory quantitative course, we analysed the use of worked-out examples by students. Our method is to combine demographic and trace data from learning-management systems with self-reports of several contemporary social-cognitive theories. Students differ not only in the intensity of using worked-out examples but also in how they positioned that usage in their learning cycle. These differences could be described both in terms of differences measured by LA trace variables and by differences in students’ learning dispositions. We conjecture that using learning dispositions with trace data has significant advantages for understanding student’s learning behaviours. Rather than focusing on low user engagement, lessons learned from LA applications should focus on potential causes of suboptimal learning, such as applying ineffective learning strategies