This Research Full Paper presents a systematic literature review on the impact of feedback features on students' subsequent learning strategies. Research indicates that providing quality feedback improves learning performance in higher education, namely in computing and engineering education. Framed by the self-regulated learning model, this enhancement results from the interplay of cognitive, metacognitive, motivational, and behavioral actions driven by feedback toward the learning goal. Such a combination of planned and selected actions is the learning strategy decision to direct the accomplishment of learning tasks. Insights of how feedback interventions lead to increased use of effective learning strategies have predominantly relied on qualitative data from self-reports. However, self-reports mainly reflect students' perceptions but cannot accurately capture the dynamic adjustments of learning strategies in the feedback process. With the growing use of learning management systems that can collect various learning analytics data, recent works have attempted to automatically generate personalized feedback based on mapping students' progress against pre-determined rules. Learning strategy alterations as a result of the various forms of feedback can be detected from the trace data. The findings of these studies provide evidence of how various feedback features are associated with the adjustment of learning strategies. This paper presents a systematic literature review that analyzes papers related to shifting learning strategies upon feedback provision to identify features that can trigger students' adoption of more effective learning strategies. The objective is to collect evidence to highlight feedback as more than information but a process to guide the proper use of learning strategies for better learning achievement. Our analysis shows a need for more studies to observe changes in learner actions due to feedback and discusses limitations in current works. With the rapid development of education data mining and deep learning models, the growing knowledge of feedback features can be potentially used with these computational models to generate learning advice to guide strategy changes for achieving better learning outcomes.

This research-to-practice paper describes a study of motivational factors in introductory programming learning. Learning to program is challenging, as students need to develop multiple skills and competencies. Motivation drives students to confront complex challenges, persevere despite obstacles, and continuously strive for improvement. However, motivation is a complex interplay of internal and external factors. Analyzing the factors that can stimulate student motivation is essential for educators when planning and implementing learning activities and contexts. Therefore, we conducted a study to a) identify factors influencing the motivation of programming students and b) analyze the evolution of students' motivation during the different phases of a programming course. The study involved 137 students enrolled in a Programming I course at a Macao higher education institution. It used the motivation section of the Motivated Strategies for Learning Questionnaire (MSLQ), which comprises 31 statements grouped into six components (Intrinsic Goal Orientation (IGO), Extrinsic Goal Orientation (EGO), Value of Activity (VAT), Control of Learning (COL), Learning Self-Efficacy (LSE), and Test Anxiety (TAX)). These components can be organized into three factors (Value Components, Expectancy Components, and Affective Components). The students were asked to answer the questionnaire in three different moments: the initial phase of the course (3–4 weeks after its start), after knowing the results of the mid-term exam, and at the end of the course. For the analysis, only the answers of the 92 students who completed the questionnaire in the three phases were considered. We applied Principal Component Analysis (PCA) to identify the evolution of the different components and factors during the course. Based on this analysis, it is possible to highlight significant variations between the various phases of the study, especially concerning the factor of Value Components. In Phase 1, participants expressed a more positive perception of the importance of the course contents, as evidenced by the VAT component. In Phase 2, a change in focus was noticed, with the prioritization of obtaining a good grade, as reflected by the EGO component. Finally, in Phase 3, there was again a reorientation of value components, with students demonstrating appreciation for the course topic, as indicated again by the VAT component. Given these results, it is possible to conclude that changes occurred in the different phases of the study, suggesting an evolutionary dynamic in the interests of participants over time.

This Research Full Paper presents a collection of evidences about the association between self-regulation variables and programming learning. Researchers have been investigating this thematic and despite the apparent benefits, it is necessary to summarize the published evidence and provide a new collection of them, which this study seeks to contribute. An observational investigation was performed in two countries with fifty-nine students, who had their SRL and programming learning metrics collected and correlated. Moreover, a systematic literature review was also conducted, and the existing evidence summarized. The results support an association between metacognitive and motivational regulatory strategies with programming learning but do not support for cognitive strategies. Our analysis shows a need for more studies to provide a solid body of knowledge on this thematic.