Jekaterina Rogaten

Use of Creative Cognition and Positive Affect in Studying: Evidence of a Reciprocal Relationship

This two-wave study examined the longitudinal relationships between positive affect in studying and the use of creative cognition in studying. Based on the broaden-and-build theory of positive emotions, the mood-as-input model, the control-process model of self-regulation of intentional behavior, and self-determination theory, it was hypothesized that positive affect will be both an antecedent and a consequence of the use of creative cognition. A sample of 130 university students completed the International Positive and Negative Affect Schedule–Short Form (I-PANAS-SF) and the Use of Creative Cognition Scale (UCCS) with reference to their overall studying experience in the first and second semesters of an academic year. A comparison of alternative structural equation models showed clear support for the reciprocal relationship between positive affect in studying and the use of creative cognition in studying. The theoretical and practical implications of these findings are outlined.

Development and validation of the short use of creative cognition scale in studying

This paper reports the development and validation of a short Use of Creative Cognition Scale in Studying (UCCS) that was inspired by the Cognitive Processes Associated with Creativity (CPAC) scale. In Study 1, items from two of the six subscales of the CPAC were excluded due to conceptual and psychometric issues to create a 21-item CPAC scale, which was administered to 517 university students. Exploratory factor analysis revealed that the 21-item CPAC scale is unidimensional. Five items were selected to create the new unidimensional UCCS. In Study 2, 696 students completed the UCCS and a set of scales measuring related constructs. Confirmatory factor analysis corroborated the unidimensional structure of the scale. The scale correlated positively with measures of flow, trait intrinsic motivation, adaptive metacognitive traits and positive affect, it correlated negatively with negative affect, and it did not correlate with core maladaptive metacognitive traits. The findings indicate that the scale is a valid and reliable tool for research and monitoring.

Assessing Learning Gains

Over the last 30 years a range of assessment strategies have been developed aiming to effectively capture students’ learning in Higher Education and one such strategy is measuring students’ learning gains. The main goal of this study was to examine whether academic performance within modules is a valid proxy for estimating students’ learning gains. A total of 17,700 Science and Social Science students in 111 modules at the Open University UK were included in our three-level linear growth-curve model. Results indicated that for students studying in Science disciplines modules, module accounted for 33% of variance in students’ initial achievements, and 26% of variance in subsequent learning gains, whereas for students studying in Social Science disciplines modules, module accounted for 6% of variance in initial achievements, and 19% or variance in subsequent learning gains. The importance of the nature of the consistent, high quality assessments in predicting learning gains is discussed.

Which first-year students are making most learning gains in STEM subjects?

ABSTRACT With the introduction of the Teaching Excellence Framework a lot of attention is focussed on measuring learning gains. A vast body of research has found that individual student characteristics influence academic progression over time. This case-study aims to explore how advanced statistical techniques in combination with Big Data can be used to provide potentially new insights into how students are progressing over time, and in particular how students’ socio-demographics (i.e. gender, ethnicity, Social Economic Status, prior educational qualifications) influence students’ learning trajectories. Longitudinal academic performance data were sampled from 4222 first-year STEM students across nine modules and analysed using multi-level growth-curve modelling. There were significant differences between white and non-White students, and students with different prior educational qualifications. However, student-level characteristics accounted only for a small portion of variance. The majority of variance was explained by module-level characteristics and assessment level characteristics.

Investigation of Temporal Dynamics in MOOC Learning Trajectories: A Geocultural Perspective

Openness, scalability, and reachability are intrinsic features of MOOCs. However, research studies in MOOCs indicated low participation from some cultural clusters, mostly from less privileged strata of the world’s population. The impeding factors are not only related to individual student characteristics, but also are related to structure and curriculum design. This proposed PhD thesis will address this stratification, performance and achievement gap. In a cross-module analysis on Open University MOOCs at FutureLearn, temporal predictive modelling was used to explore learners’ background, regional belonging and behavioral patterns that contribute towards engagement, and overall performance. Later on, clustering and temporal process mining will be employed to observe end-to-end processes of learning. Behavioral traces and learning trajectories for different clusters of learners will be explored in a variety of MOOC Learning Designs (LD). The research findings aim to provide useful actionable insights on how adaptations in LD can make MOOCs more inclusive and diverse.