Profiles of Motivated Self-Regulation in College Computer Science Courses: Differences in Major versus Required Non-Major Courses

Shell, Duane F.; Soh, Leen Kiat

doi:10.1007/s10956-013-9437-9

Cited by 88 publications

(168 citation statements)

References 46 publications

Supporting

Mentioning

144

Contrasting

Unclassified

Order By: Relevance

“…The majority of the questionnaires used to construct our survey were adopted from Ref. 4; however, when certain items from that study were not readily available in the literature, other comparable ones were used in order to probe similar student characteristic variables. These items are those of the Approaches and Study Skills Inventory for Students and the Zimbardo Time Perspective Inventory.…”

Section: Methods and Analysismentioning

confidence: 99%

“…We are interested in how student study strategies and motivations may play a role in explaining these differences in student success. In previous studies outside of physics education research, five distinct learning profiles have been identified among several student populations [4]. We investigate the presence of these five previously identified learning profiles among students enrolled in our algebra-based introductory studio-mode physics courses.…”

Section: Introductionmentioning

confidence: 92%

“…4 for a review). The five profiles are: (1) a strategic (Strat) profile of a student motivated to learn and retain the subject material, using whatever self-regulatory strategies are needed to do so; (2) a knowledge-building (KB) profile of a student intrinsically motivated to learn and understand the subject material, but less actively engaged with the course; (3) a surface (Sur) learning profile of a student primarily concerned with passing the course with little engagement in the subject material; (4) an apathetic (Apa) profile of an unmotivated student that, though wanting to pass the class, invests minimal engagement and personal interest in learning the subject material; and (5) a learned helpless (LH) profile of a student motivated to pass the course, but unable to optimize self-regulatory study strategies [4].…”

Section: Five-profile Frameworkmentioning

confidence: 99%

“…In order to do this, we compile a student characteristic survey from several published and validated questionnaires, similar to those used in Ref. 4, which probes students' strategic self-regulation and motivational characteristics. We establish our survey's reliability and construct validity in our sample.…”

Section: Introductionmentioning

confidence: 99%

“…We then use cluster analysis to explore the existence of these five learning profiles among our students. This cluster analysis is "person-centered" in that it uses students' individual responses to find common, coherent groups among the students [4].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Exploring Student Learning Profiles in Algebra-based Studio Physics: A Person-Centered Approach

Pond

Chini

2015

2015 Physics Education Research Conference Proceedings

View full text Add to dashboard Cite

Abstract. As part of a project to explore successful strategies for using studio methods, such as SCALE-UP, we explore strategic self-regulatory and motivational characteristics of students in said courses at three universities with varying student populations and differing success in studio-mode courses. We survey students using compiled questions from several existing questionnaires designed to measure student characteristics such as attitudes toward and motivations for learning physics, organization of scientific knowledge, experiences outside the classroom, and demographics. Other studies have identified five distinct learning profiles across varying student populations. Using a person-centered approach, we utilize cluster analysis methods to group students into learning profiles to better understand the study strategies and motives of algebra-based studio physics students. We present results from firstsemester and second-semester studio-mode introductory physics courses across three universities. We identify these five distinct learning profiles, found in previous studies, to be present within our student population.

show abstract

Section: Methods and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Five-profile Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploring Student Learning Profiles in Algebra-based Studio Physics: A Person-Centered Approach

Pond

Chini

2015

2015 Physics Education Research Conference Proceedings

View full text Add to dashboard Cite

show abstract

Computational thinking in higher education: A review of the literature

Lyon

Magana

2020

Comp Applic In Engineering

View full text Add to dashboard Cite

Computational thinking is of growing interest to the science, technology, engineering, and mathematics (STEM) education research community. Calls from national agencies look to increase computation in STEM education. This review identifies key areas for future study by reviewing recent empirical studies that investigate computational thinking in teaching and learning contexts within higher education. Using a systematic process, this review identified four different databases for peer‐reviewed research articles using keywords. Results were evaluated against inclusion and exclusion criteria. Studies were analyzed for types of methods, target population, the role of computational thinking, pedagogical designs used, and significant findings of the study. This process resulted in a final set of 13 studies. The results indicate that computational thinking research in higher education is growing, yet there are opportunities for more research. The findings of this study highlight the need for more concrete definitions and implementations of computational thinking within higher education spaces.

show abstract

Motivational and Self‐Regulated Learning Profiles of Students Taking a Foundational Engineering Course

Nelson

Shell

Husman

et al. 2014

J of Engineering Edu

Self Cite

136

123

View full text Add to dashboard Cite

Background Technical, nonengineering required courses taken at the onset of an engineering degree provide students a foundation for engineering coursework. Students who perform poorly in these foundational courses, even in those tailored to engineering, typically have limited success in engineering. A profile approach may explain why these courses are obstacles for engineering students. This approach examines the interaction among motivation and self‐regulation constructs. Purpose (Hypothesis) This project sought to determine what motivational and self‐regulated learning profiles engineering students adopt in foundational courses. We hypothesized that engineering students would adopt profiles associated with maladaptive motivational beliefs and self‐regulated learning behaviors. The effects of profile adoption on learning and differences associated with student major, minor, and gender were analyzed. Design/Method Five hundred and thirty‐eight students, 332 of them engineering majors, were surveyed on motivation and self‐regulation variables. Data were analyzed from a learner‐centered profile approach using cluster analysis. Results We obtained a five‐cluster learning profile solution. Approximately 83% of engineering students enrolled in an engineering‐tailored foundational computer science course adopted maladaptive profiles. These students learned less than those who adopted adaptive learning profiles. Profile adoption depended on whether a student was considering a major or minor in computer science or not. Conclusions Findings indicate the motivational and self‐regulated learning profiles that engineering students adopt in foundational courses, why they do so, and what profile adoption means for learning. Our findings can guide instructors in providing motivational beliefs and self‐regulated learning scaffolds in the classroom.

show abstract

Profiles of Motivated Self-Regulation in College Computer Science Courses: Differences in Major versus Required Non-Major Courses

Cited by 88 publications

References 46 publications

Exploring Student Learning Profiles in Algebra-based Studio Physics: A Person-Centered Approach

Exploring Student Learning Profiles in Algebra-based Studio Physics: A Person-Centered Approach

Computational thinking in higher education: A review of the literature

Motivational and Self‐Regulated Learning Profiles of Students Taking a Foundational Engineering Course

Contact Info

Product

Resources

About