The aim of this research was to determine the degree to which undergraduate students' learning approach, academic achievement and satisfaction were determined by the combination of an intrapersonal factor (self-regulation) and a interpersonal factor (contextual or regulatory teaching). The hypothesis proposed that greater combined regulation (internal and external) would be accompanied by more of a deep approach to learning, more satisfaction and higher achievement, while a lower level of combined regulation would determine a surface approach, less satisfaction and lower achievement. Within an ex post facto design by selection, 1036 university students completed validated questionnaires using an online tool. Several multivariate analyses were conducted. Results showed that the combination of self-regulation and external regulation can be ordered as levels along a five-point scale or heuristic. These levels linearly determine type of learning approach, academic achievement and satisfaction. Implications are established for quality and improvement of the teaching and learning process at university.

The two studies reported here explored the factor structure of the newly constructed Writing Achievement Goal Scale (WAGS), and examined relationships among secondary students' writing achievement goals, writing self-efficacy, affect for writing, and writing achievement. In the first study, 697 middle school students completed the WAGS. A confirmatory factor analysis revealed a good fit for this data with a three-factor model that corresponds with mastery, performance approach, and performance avoidance goals. The results of Study 1 were an indication for the researchers to move forward with Study 2, which included 563 high school students. The secondary students completed the WAGS, as well as the Self-efficacy for Writing Scale, and the Liking Writing Scale. Students also self-reported grades for writing and for language arts courses. Approximately 6 weeks later, students completed a statewide writing assessment. We tested a theoretical model representing relationships among Study 2 variables using structural equation modeling including students' responses to the study scales and students' scores on the statewide assessment. Results from Study 2 revealed a good fit between a model depicting proposed relationships among the constructs and the data. Findings are discussed relative to achievement goal theory and writing.

The study focused on the analysis of linear relations between personality, self-regulation, coping strategies and achievement emotions. The main objective was to establish a model of linear, empirical, associative to infer needs and proposals for intervening in emotional health in the different profiles of university students. A total of 642 undergraduate students participated in this research. Evidence of associative relations between personality factors, self-regulation and coping strategies was found. The neuroticism factor had a significant negative associative relationship with Self-Regulation both globally and in its factors; especially important was its negative relation to decision making, and coping strategies focused in emotion. The results of Structural Equation Model showed an acceptable model of relationships, in each emotional context. Results and practical implications are discussed.

In this study, we work towards a strategy to measure and enhance the quality of interactions in discussion forums at scale. We present a machine learning (ML) model which identifies the phase of cognitive presence exhibited by a student’s post and suggest future applications of such a model to help online students develop higher-order thinking. We collect discussion forum transcript data from two online courses: CS1301 (an introductory computer programming MOOC) offered by edX and CS6601 (a graduate course on artificial intelligence) which uses the Piazza online discussion tool. We manually code a random sample of students’ posts based on the Community of Inquiry coding scheme and explore trends in cognitive presence within and across the courses. We further use this coded data to analyze the relationship between students’ observed cognitive presence and course grades. In terms of testing and building an ML model, we use a Bidirectional Encoder Representations from Transformers model that uses a deep learning technique to train large text corpus and fine-tune the language model. Our results suggest that deeper cognitive engagement with course concepts, as expressed by higher cognitive presence, are associated with better learning outcomes for students in both course settings. Our ML approach achieves 92.5% accuracy on the classification task, motivating the use of ML for instructional interventions in online courses. We expect that our research study will not only contribute to extending the literature on cognitive presence but also have a beneficial impact on online instructors or curriculum developers in higher education.

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