What learning analytics based prediction models tell us about feedback preferences of students

Learning analytics (LA) seeks to enhance learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators (Siemens & Long, 2011). This study examined the use of preferred feedback modes in students by using a dispositional learning analytics framework, combining learning disposition data with data extracted from digital systems. We analyzed the use of feedback of 1062 students taking an introductory mathematics and statistics course, enhanced with digital tools. Our findings indicated that compared with hints, fully worked-out solutions demonstrated a stronger effect on academic performance and acted as a better mediator between learning dispositions and academic performance. This study demonstrated how e-learners and their data can be effectively re-deployed to provide meaningful insights to both educators and learners

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

The Dark Side of the Self-Determination Theory and Its Influence on the Emotional and Cognitive Processes of Students in Physical Education

Amongst the main objectives of physical education (PE) classes is the consolidation of healthy lifestyle habits in young people and adolescents. Nonetheless, these classes can also provide the basis from which adverse experiences are generated which affect students’ perceptions of these classes. Previously conducted studies have focused on motivational processes and not on emotional processes, nor on the way in which students learn. The objective of the present study was to explore the dark side of the self-determination theory, its influence on emotional intelligence and the meta-cognitive strategies of students. Methodology: A total of 1602 young people undertaking secondary education participated, with self-reported ages between 13 and 19 years. The following questionnaires were utilized: Controlling Coach Behaviors Scale, Frustration of Psychological Needs in PE classes Scale, Emotional Intelligence in PE Scale and Motivated Strategies for Learning Questionnaire. A structural equation model was developed which explained causal associations between the study variables. Results: Psychological control positively predicted each one of the sub-factors of frustration of psychological needs. Frustration of psychological needs negatively predicted emotional intelligence. Finally, emotional intelligence positively predicted meta-cognitive thinking. Conclusions: The influence and importance of the teaching style adopted by teachers is indicated, in addition to the effect of students’ psychological experiences on emotions and learning strategie

Linking academic emotions and student engagement: mature-aged distance students’ transition to university

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Further and Higher Education on 2013, available online: https://www.tandfonline.com/doi/full/10.1080/0309877X.2014.895305Research into both student engagement and student emotions is increasing, with widespread agreement that both are critical determinants of student success in higher education. Less researched are the complex, reciprocal relationships between these important influences. Two theoretical frameworks inform this paper: Pekrun’s taxonomy of academic emotions and Kahu’s conceptual framework of student engagement. The prospective qualitative design aims to allow a rich understanding of the fluctuating and diverse emotions that students experience during the transition to university and to explore the relationships between academic emotions and student engagement. The study follows 19 mature-aged (aged 24 and over) distance students throughout their first semester at university, using video diaries to collect data on their emotional experiences and their engagement with their study. Pre and post-semester interviews were also conducted. Findings highlight that different emotions have different links to engagement: as important elements in emotional engagement, as inhibitors of engagement and as outcomes that reciprocally influence engagement. There are two key conclusions. First, student emotions are the point of intersection between the university factors such as course design and student variables such as motivation and background. Second, the flow of influence between emotions, engagement, and learning is reciprocal and complex and can spiral upwards towards ideal engagement or downwards towards disengagement and withdrawal.Publishe

The effect of online English learners’ perceived teacher support on self-regulation mediated by their self-efficacy

This study explored the relationships among online English learners’ perceived teacher support, self-efficacy, and self-regulation in online learning based on social cognitive theory. Structural equation modeling (SEM) with bootstrapping estimation was conducted using data from 220 online English learners engaged in blended learning on the Chinese University MOOC platform. The results showed that online English learners’ perceived teacher support positively influenced their self-efficacy and self-regulation. Moreover, self-efficacy was found to mediate the relationship between their perceived teacher support and self-regulation. On the whole, the findings detailed the effect of English learners’ perceived teacher support on their self-efficacy and self-regulation, as well as empirically identifying the mediation effect of self-efficacy in the relationship between perceived teacher support and self-regulation in an online learning environment. Related pedagogical implications for teacher online teaching, student online learning, and the Chinese University MOOC platform, and limitations were discussed.En el presente estudio, basado en la teoría social cognitiva, se explora la relación entre el apoyo docente percibido por los alumnos de inglés online, la autoeficacia y la autorregulación en el aprendizaje online. Se realizó un modelo de ecuaciones estructurales (MES), con la estimación Bootstrap utilizando los datos de 220 alumnos de inglés online de una universidad politécnica que participan en el aprendizaje combinado en la plataforma MOOC de las Universidades Chinas. Los resultados mostraron que el apoyo docente percibido por los alumnos de inglés online influenció positivamente su autoeficacia y autorregulación. Además, se descubrió que la autoeficacia podía mediar la relación entre el apoyo docente percibido y la autorregulación. En general, los resultados describieron con detalle el efecto del apoyo docente percibido por los alumnos de inglés online sobre su autoeficacia y autorregulación, e identificaron empíricamente el efecto mediador de la autoeficacia en la relación entre el apoyo docente percibido y la autorregulación en un entorno de aprendizaje online. Por último, se discutieron las implicaciones pedagógicas para la enseñanza online de los profesores, el aprendizaje online de los alumnos y la plataforma MOOC de las Universidades Chinas, así como las limitaciones del estudio.North University of China and a provincial research grant (No. J2020180) in Chin

Determinants and outcomes of motivation in health professions education: a systematic review based on self-determination theory

Purpose: This study aimed at conducting a systematic review in health professions education of determinants, mediators and outcomes of students’ motivation to engage in academic activities based on the self-determination theory’s perspective. Methods: A search was conducted across databases (MEDLINE, CINHAL, EMBASE, PsycINFO, and ERIC databases), hand-search of relevant journals, grey literature, and published research profile of key authors. Quantitative and qualitative studies were included if they reported research in health professions education focused on determinants, mediators, and/or outcomes of motivation from the self-determination and if meeting the quality criteria. Results: A total of 17 studies met the inclusion and quality criteria. Articles retrieved came from diverse locations and mainly from medical education and to a lesser extent from psychology and dental education. Intrapersonal (gender and personality traits) and interpersonal determinants (academic conditions and lifestyle, qualitative method of selection, feedback, and an autonomy supportive learning climate) have been reported to have a positive influence on students’ motivation to engage in academic activities. No studies were found that tested mediation effects between determinants and students’ motivation. In turn, students’ self-determined motivation has been found to be positively associated with different cognitive, affective, and behavioural outcomes. Conclusion: This study has found that generally, motivation could be enhanced by changes in the educational environment and by an early detection of students’ characteristics. Doing so may support future health practitioners’ self-determined motivation and positively influence how they process information and their emotions and how they approach their learning activities

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