Student engagement and perceptions of blended-learning of a clinical module in a veterinary degree program.

Blended learning has received much interest in higher education as a way to increase learning efficiency and effectiveness. By combining face-to-face teaching with technology-enhanced learning through online resources, students can manage their own learning. Blended methods are of particular interest in professional degree programs such as veterinary medicine in which students need the flexibility to undertake intra- and extramural activities to develop the range of competencies required to achieve professional qualification. Yet how veterinary students engage with blended learning activities and whether they perceive the approach as beneficial is unclear. We evaluated blended learning through review of student feedback on a 4-week clinical module in a veterinary degree program. The module combined face-to-face sessions with online resources. Feedback was collected by means of a structured online questionnaire at the end of the module and log data collected as part of a routine teaching audit. The features of blended learning that support and detract from students’ learning experience were explored using quantitative and qualitative methods. Students perceived a benefit from aspects of face-to-face teaching and technology-enhanced learning resources. Face-to-face teaching was appreciated for practical activities, whereas online resources were considered effective for facilitating module organization and allowing flexible access to learning materials. The blended approach was particularly appreciated for clinical skills in which students valued a combination of visual resources and practical activities. Although we identified several limitations with online resources that need to be addressed when constructing blended courses, blended learning shows potential to enhance student-led learning in clinical courses

Learning Feedback Based on Dispositional Learning Analytics

The combination of trace data captured from technology-enhanced learning support systems, formative assessment data and learning disposition data based on self-report surveys, offers a very rich context for learning analytics applications. In previous research, we have demonstrated how such Dispositional Learning Analytics applications not only have great potential regarding predictive power, e.g. with the aim to promptly signal students at risk, but also provide both students and teacher with actionable feedback. The ability to link predictions, such as a risk for drop-out, with characterizations of learning dispositions, such as profiles of learning strategies, implies that the provision of learning feedback is not the end point, but can be extended to the design of learning interventions that address suboptimal learning dispositions. Building upon the case studies we developed in our previous research, we replicated the Dispositional Learning Analytics analyses in the most recent 17/18 cohort of students based on the learning processes of 1017 first-year students in a blended introductory quantitative course. We conclude that the outcomes of these analyses, such as boredom being an important learning emotion, planning and task management being crucial skills in the efficient use of digital learning tools, help both predict learning performance and design effective interventions

Understanding the Role of Time on Task in Formative Assessment: The Case of Mathematics Learning

Mastery data derived from formative assessments constitute a rich data set in the development of student performance prediction models. The dominance of formative assessment mastery data over use intensity data such as time on task or number of clicks was the outcome of previous research by the authors in a dispositional learning analytics context. Practical implications of these findings are far reaching, contradicting current practices of developing (learning analytics based) student performance prediction models based on intensity data as central predictor variables. In this empirical follow-up study using data of 2011 students, we search for an explanation for time on task data being dominated by mastery data. We do so by investigating more general models, allowing for nonlinear, even non-monotonic, relationships between time on task and performance measures. Clustering students into subsamples, with different time on task characteristics, suggests heterogeneity of the sample to be an important cause of the nonlinear relationships with performance measures. Time on task data appear to be more sensitive to the effects of heterogeneity than mastery data, providing a further argument to prioritize formative assessment mastery data as predictor variables in the design of prediction models directed at the generation of learning feedback

Learning Analytics and the Measurement of Learning Engagement

The measurement of learning engagement is a major research theme, both in the learning analytics community and the broader area of educational research. The complexity of conceptualizing as well as operationalizing the construct of engagement generates a wide range of instruments, such as self-report surveys, log data from technology-enhanced learning systems, think-aloud and tests. In this empirical work, we investigate the alignment of behavioural traces of engagement with self-report measures and their impact on academic performance. The unique contribution of this study was the integration of temporal, behavioural, affective, and cognitive dimensions of engagement by combing digital traces at three different learning phases with self-report, formative as well as summative assessments. Using a two-step cluster analysis based on data from 1,027 undergraduate students in a first-year 8-week statistics course, we identified four distinct temporal engagement patterns (i.e. nonactive, active before tutorial, active before quiz, and active before exams). Our analysis showed that early engagement (i.e. before tutorial) was significantly associated with course performance and self-report measures, while late engagement patterns had weaker correlations. This study shed further lights on a potential source of heterogeneity and collinearity in engagement measures (i.e. timing of engagement) that should be accounted for in learning analytics model. In order to design effective intervention, it is crucial to consider different profiles of learners based on their engagement patterns as well as the temporal relation between trace data, self-report, and academic performance

Student Learning Preferences in a Blended Learning Environment: Investigating the Relationship Between Tool Use and Learning Approaches

Building on research into the demands on students’ self-regulated learning when learning about conceptually rich domains with computer-based learning environments (CBLEs) (Azevedo, Current perspectives on cognition, learning, and instruction, 2008; Lajoie and Azevedo, Handbook of educational psychology, 2006), our study focuses on the research question how students self-regulate their learning in a blended learning environment. In the teaching of introductory statistics to first-year students in economics and business, the Maastricht University uses a blended learning environment that allows students to individualize by attuning available learning tools to personal preferences. The blended learning environment consists of tutorials based on the problem-based learning principle and independent learning driven by learning goals produced by these tutorials; a sequence of traditional lectures, and an electronic learning environment based on the adaptive tutorial system Assessment and LEarning in Knowledge Spaces (ALEKS). Only participation in tutorial sessions is required; the usage of other components can be set according to individual preferences. The main reason to introduce the blended learning environment had been the need to accommodate a very heterogeneous inflow of students, transferring from very different secondary school systems with large differences in prior knowledge of statistics. For example, whereas a part of prospective students has had prior schooling in statistics, the majority of the inflow is educated within secondary school systems that lack coverage of statistics. The principle of repeated formative, adaptive testing that serves as the kernel of the ALEKS tool and steers all student learning and practicing makes the tool tailored to bridge short falling prior knowledge. However, on top of accommodating cognitive differences, the tool appeared to accommodate differences in learning styles. In this study, we will focus on this last aspect, by investigating the relationship between the intensity of the use of the electronic learning environment ALEKS and student background characteristics, such as learning style preferences, achievement motivation, self-concept constructs and subject attitudes. Data of about 4,650 freshmen from six subsequent cohorts participating in this course are used. Correlational analyses suggest that especially less academically prepared students profit most from the e-learning facilities in the blended learning environment: intensity of e-learning is positively correlated with the step-wise (surface) learning style and the dependency of a stimulating learning environment, and negatively correlated with mathematical self-concept and attitudes towards the subject statistics. These findings suggest that facilitating different learning approaches in the freshman program might help in the transition of less academically adapted students

Student Learning Preferences in a Blended Learning Environment: Investigating the Relationship Between Tool Use and Learning Approaches

Building on research into the demands on students’ self-regulated learning when learning about conceptually rich domains with computer-based learning environments (CBLEs) (Azevedo, Current perspectives on cognition, learning, and instruction, 2008; Lajoie and Azevedo, Handbook of educational psychology, 2006), our study focuses on the research question how students self-regulate their learning in a blended learning environment. In the teaching of introductory statistics to first-year students in economics and business, the Maastricht University uses a blended learning environment that allows students to individualize by attuning available learning tools to personal preferences. The blended learning environment consists of tutorials based on the problem-based learning principle and independent learning driven by learning goals produced by these tutorials; a sequence of traditional lectures, and an electronic learning environment based on the adaptive tutorial system Assessment and LEarning in Knowledge Spaces (ALEKS). Only participation in tutorial sessions is required; the usage of other components can be set according to individual preferences. The main reason to introduce the blended learning environment had been the need to accommodate a very heterogeneous inflow of students, transferring from very different secondary school systems with large differences in prior knowledge of statistics. For example, whereas a part of prospective students has had prior schooling in statistics, the majority of the inflow is educated within secondary school systems that lack coverage of statistics. The principle of repeated formative, adaptive testing that serves as the kernel of the ALEKS tool and steers all student learning and practicing makes the tool tailored to bridge short falling prior knowledge. However, on top of accommodating cognitive differences, the tool appeared to accommodate differences in learning styles. In this study, we will focus on this last aspect, by investigating the relationship between the intensity of the use of the electronic learning environment ALEKS and student background characteristics, such as learning style preferences, achievement motivation, self-concept constructs and subject attitudes. Data of about 4,650 freshmen from six subsequent cohorts participating in this course are used. Correlational analyses suggest that especially less academically prepared students profit most from the e-learning facilities in the blended learning environment: intensity of e-learning is positively correlated with the step-wise (surface) learning style and the dependency of a stimulating learning environment, and negatively correlated with mathematical self-concept and attitudes towards the subject statistics. These findings suggest that facilitating different learning approaches in the freshman program might help in the transition of less academically adapted students