Creativity and positive emotions in studying: Novel possibilities for improving students’ learning

Research on students’ learning identified that positive affect is a strong predictor of better academic performance even when statistically controlling for effects of prior academic performance and approaches to learning (e.g., Rogaten, Moneta & Spada, 2013). A variable that has been found to strongly link with positive affect in studying is use of creative cognition, which is the habit to deploy one’s own creative ability to an endeavour (Rogaten & Moneta, in press). Based on the broaden-and-build theory (Fredrickson, 1998), the mood-as-input model (Martin et al., 1993), the control-process model (Carver & Scheier, 2001), and self-determination theory (Deci & Ryan, 1985), it was hypothesised that positive affect will be both an antecedent and a consequence of use of creative cognition in studying. 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 use of creative cognition in studying. This is the first study that found the longitudinal relationship between use of creative cognition in studying and subsequent positive affect in studying, which opens novel possibilities for interventions. Well-designed curricula, assessments and training programs that foster the use of creative cognition in studying may increase students’ positive affect and engagement in studying and, in turn, improve their learning and academic performance

Adaptive-Positive vs. Maladaptive-Negative Structures and Processes in Learning: Towards the Comprehensive Model of Academic Performance

The goal of this Ph.D. research was to develop an empirical foundation suitable for designing educational interventions and programmes aiming to improve students’ learning. In order to achieve this, a series of studies was conducted that supported the development and test of a comprehensive, chained mediation model of academic performance. The proposed chained mediation model comprised of adaptive-positive and maladaptive-negative submodels. The adaptive-positive submodel hypothesised firstly that trait intrinsic motivation and adaptive metacognition would facilitate the use of creative cognition in studying (first-level mediator). Secondly, the model hypothesised that the use of creative cognition in studying would lead to the experience of positive affect in studying, and to the development of adaptive approaches to studying (second-level mediators). Finally, the submodel hypothesised that positive affect in studying and adaptive approaches to studying would facilitate academic performance. The maladaptive-negative submodel hypothesised firstly that trait extrinsic motivation and maladaptive metacognition would lead to evaluation anxiety (first-level mediator). Secondly, the model hypothesised that evaluation anxiety would lead to the experience of negative affect in studying, and to the development of a maladaptive approach to studying (second-level mediators). Finally, the submodel hypothesised that negative affect in studying and the maladaptive approach to studying would undermine academic performance. A total of five studies were conducted employing 2140 university students. Study 1 tested the effects of approaches to studying and positive and negative affect in studying on students’ academic performance. The results strongly indicated that positive and negative affect in studying explains students’ academic performance better than approaches to studying. Studies 2 and 3 developed and validated a new Use of Creative Cognition Scale (UCCS), which measures students’ tendency to deploy creative thinking strategies in studying. Study 4 tested longitudinal relationship between positive affect in studying and the use of creative cognition. The results supported the reciprocal, longitudinal relationship between the two constructs. Finally, Study 5 proposed and tested the comprehensive, chained mediation model of academic performance. Structural equation modelling (SEM) showed that the model explained 90% of the variance in students’ academic performance, and that prior academic performance and positive affect in studying were the only significant correlates. The use of creative cognition in studying was the strongest correlate of positive affect in studying, and also mediated the effect of trait intrinsic motivation and adaptive metacognition on positive affect. Overall, adaptive-positive psychological variables were superior to maladaptive-negative ones in explaining students’ academic performance. Therefore, educational interventions aiming to enhance students’ learning should target particularly adaptive-positive psychological variables in students. The possible model-based intervention is outlined

A Critical Review of Learning Gains Methods and Approaches

In the last five years, there has been an increased interest across the globe, and in the United Kingdom in particular, to define, conceptualise and measure learning gains. The concept of learning gains, briefly summarised as the improvement in knowledge, skills, work-readiness and personal development made by students during their time spent in higher education, has been hailed by some as an opportunity to measure ‘excellence’ in teaching. This chapter will review some of the common definitions and the methods employed in research on learning gains. Secondly, we will provide a critical evaluation of the computational aspects of learning gains (e.g., raw gain, normalised gain). Finally, we will critically reflect upon the lessons learnt and what is not yet known in terms of learning gains

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

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 4,222 first year STEM students across nine modules and analysed using multilevel growth-curve modeling. 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

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

Mirror, mirror, on your wall: The impact of fashion on eating difficulties

Ella is a young woman who likes hanging around with her friends, dress up to the occasion and spends quite a lot of time on her phone. As time goes by and without her necessarily making an active choice, she gradually finds herself engaging in restricted eating and quite strict dieting. Why do women like Ella feel that they need to change their eating patterns? Fashion and beauty advertisements are all around us. You see fashion advertisements on your daily commute to work, on billboards, in shop windows during your leisurely walks, in multiple magazines scattered around coffee tables, on TV around every 20 minutes and of course, on your social media feed. These advertisements repeatedly present how you should look, and for women the message is quite clear; one needs to be relatively tall, slim, young and Caucasian. Men are also increasingly becoming fashion conscious, although beauty standards for males are more diverse. In general, they experience less pressure to fit stereotypical images and standards pertaining to physical appearance. In this chapter, we will look at how fashion impacts on our relationship with food, primarily focusing on how the acceptance of socially constructed beauty standards affects eating behaviour

Approaches to researching digital-pedagogical competence development in VE-based teacher education

For the past two decades, Virtual Exchange (VE) has enjoyed increasing popularity in university education, including initial (language) teacher education programmes (O’Dowd, 2018). Collaborating online with colleagues and students from different cultural backgrounds and educational systems has allowed trainees to experience and reflect on issues related to technology and pedagogy in authentic linguistic and intercultural contexts. In 2017/2018, the Evaluating and Upscaling Telecollaborative Teacher Education (EVALUATE) project – an Erasmus+ funded European Policy Experimentation (EPE) – collected and analysed data from VEs across the curriculum involving over 1,000 participants at Initial Teacher Education (ITE) institutions in Europe and beyond. Here, we specifically focus on the impact of VE on their digital-pedagogical competence development. Following a mixed method design we used the Technological PedagogicalContent Knowledge (TPACK) work of Mishra and Koehler (2006) and Schmidt et al. (2009) in a pre-post-test manner. These were complemented by qualitative content analysis of prompted diary entries at key stages during the exchanges to collect further evidence of existing and emerging digital-pedagogical skills among the trainees. Based on one case study of a German-Polish EVALUATE exchange we will exemplify the aforementioned research methods and associated challenges. We will illustrate the urgent need for initial and in-service teacher education that combines technology and pedagogy and argue for VE as an ideal context to this effect. Finally, we will demonstrate how the chosen research approach has contributed to providing the kind of evidence required by education administrators and policy makers for a systematic integration of VE into teacher education programmes

Investigating Variation in Learning Processes in a FutureLearn MOOC

Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of engagement in the course. This study investigates the detailed processes of engagement using educational process mining (EPM) in a FutureLearn science course (N = 2086 learners) and applying an established taxonomy of learning design to classify learning activities. The analyses were performed on three groups of learners categorised based upon their clicking behaviour. The process-mining results show at least one dominant pathway in each of the three groups, though multiple popular additional pathways were identified within each group. All three groups remained interested and engaged in the various learning and assessment activities. The findings from this study suggest that in the analysis of voluminous MOOC data there is value in first clustering learners and then investigating detailed progressions within each cluster that take the order and type of learning activities into account. The approach is promising because it provides insight into variation in behavioural sequences based on learners’ intentions for earning a course certificate. These insights can inform the targeting of analytics-based interventions to support learners and inform MOOC designers about adapting learning activities to different groups of learners based on their goals

A multi-level longitudinal analysis of 80,000 online learners: Affective-Behaviour-Cognition models of learning gains

One of the challenges facing higher education is understanding what counts for an excellent educational outcome. Historically academic performance was a variable of choice for measuring ‘excellence’ in education, but more recently a concept of learning gain, which can be defined as change in knowledge, skills and personal development across time (e.g., Andrews et al., 2011; Boyas et al., 2012) gained momentum. Educational research also mainly looked at cognitive gain largely ignoring affective changes (attitude) and behaviour (Tempelaar et al., 2015a). Current research aims to address this gap by developing and testing an Affective-Behaviour-Cognition model of learning gains using longitudinal multilevel modelling. The learner-generated affective-behaviour-cognition data was retrieved from university database for 80,000+ undergraduate students who started their degree in autumn 2013/14. The preliminary multilevel modelling revealed that cognitive and behaviour learning gains are well explained by the hypothesised Affective-Behaviour-Cognition model, whereas the more complex affective learning gains model needs further refinement. The main strength of this research is that approach used is a practical and scalable solution that could be used by teachers, learners, higher education institutions and the sector as a whole in facilitating students’ learning gains by further improving and personalising provision of higher education

The impact of virtual exchange on TPACK and foreign language competence: reviewing a large-scale implementation across 23 virtual exchanges

Several studies on Virtual Exchange (VE) have highlighted positive learning experiences, increases in technological pedagogical and content skills (TPACK) and foreign language (FL) competence. However, most VE research to date use qualitative or descriptive case-studies of how VEs have been implemented, and what “might” have worked. In this large-scale quantitative two-study design, we explored how 622 pre-service teachers developed TPACK skills and (perceived) FL competence over time in 23 VEs across 34 institutions in 16 countries. In Study 1, we used a (quasi-) experimental design of 3 VEs in an experimental (n = 151) or control group (n = 77) to explore the impact on TPACK. In Study 2, we used a larger sample of 20 VEs and 394 participants to replicate and contrast the findings from Study 1 in a broader context. In contrast to our expectations, participants in the experimental condition did not have higher TPACK skills growth relative to the control condition in Study 1, which was further confirmed in Study 2. Nonetheless, in Study 2 pre-existing TPACK skills influenced the development of (perceived) FL competence over time, whereby those participants who further strengthened their TPACK skills during the VE were more likely to nurture FL competence. A major lesson from this large-scale implementation is that VEs do not generate TPACK skills and FL competence by osmosis. We encourage CALL researchers to carefully reflect on any positive or negative finding that something has “worked” when there is no comparison or control group included