College Students’ Conceptions of Learning of and Approaches to Learning Computer Science

2019 ◽  
Vol 58 (3) ◽  
pp. 662-686 ◽  
Author(s):  
Karthikeyan Umapathy ◽  
Albert D. Ritzhaupt ◽  
Zhen Xu
Keyword(s):  
College Students ◽  
Computer Science ◽  
Learning Strategies ◽  
Approaches To Learning ◽  
Internal Consistency Reliability ◽  
Stepwise Multiple Regression ◽  
Conceptions Of Learning ◽  
Approach To Learning ◽  
Research Institution ◽  
Moderating Factors

The purpose of this research was to examine college students’ conceptions of learning computer science and approaches to learning computer science and to examine the relationships among these two important constructs and possible moderating factors. Student data ( N = 193) were collected using the conceptions of learning computer science and the approaches to learning computer science surveys at one public research institution in the southeastern United States. Data were analyzed with descriptive statistics, Confirmatory Factor Analysis models, internal consistency reliability, Pearson correlations, stepwise multiple regression models, and Multivariate Analysis of Variance models. The results suggest that college students most favorably employ a deep strategy approach for learning computer science in which prior knowledge is activated and meaningful learning strategies are used. College students appear to be more extrinsically motivated to learn computer science than intrinsically. Higher level learning conceptions are associated with a deep strategy approach to learning (e.g., Seeing in a new way) whereas low-level conceptions are associated with a surface strategy (e.g., Memorizing) approach to learning. Male college students have slightly higher conceptions of programming than their female counterparts. The findings are discussed and both limitations and delimitations of the study are enumerated.

A Phenomenographic Analysis of College Students’ Conceptions of and Approaches to Programming Learning: Insights From a Comparison of Computer Science and Non-Computer Science Contexts

2021 ◽  
pp. 073563312199595
Author(s):  
Te-Lien Chou ◽  
Kai-Yu Tang ◽  
Chin-Chung Tsai
Keyword(s):  
College Students ◽  
Computer Science ◽  
Learning Outcomes ◽  
Approaches To Learning ◽  
Digital Age ◽  
Conceptions Of Learning ◽  
Community Interactions ◽  
Learning Programming ◽  
Northern Taiwan ◽  
Rote Memory

Programming learning has become an essential literacy for computer science (CS) and non-CS students in the digital age. Researchers have addressed that students’ conceptions of learning influence their approaches to learning, and thus impact their learning outcomes. Therefore, we aimed to uncover students’ conceptions of programming learning (CoPL) and approaches to programming learning (APL), and analyzed the differences between CS and non-CS students. Phenomenographic analysis was adopted to analyze 31 college students (20 CS-related, and 11 not) from northern Taiwan. Results revealed six categories of CoPL hierarchically: 1. memorizing concepts, logic, and syntax, 2. computing and practicing programming writing, 3. expressing programmers’ ideas and relieving pressure, 4. applying and understanding, 5. increasing one’s knowledge and improving one’s competence, and 6. seeing in a new way. Four categories of APL were also found, namely: 1. copying from the textbook, teachers, or others, 2. rote memory, 3. multiple exploration attempts, and 4. online or offline community interactions. Furthermore, we found that most CS students held higher level CoPL (e.g., seeing in a new way) than non-CS students. However, compared with non-CS students, CS students adopted more surface approaches to learning programming, such as copying and rote memory. Implications are discussed.

scholarly journals Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Dragan Gasevic ◽  
Jelena Jovanovic ◽  
Abelardo Pardo ◽  
Shane Dawson
Keyword(s):  
Academic Performance ◽  
Learning Strategies ◽  
Flipped Classroom ◽  
Approaches To Learning ◽  
Self Report ◽  
Deep Approach ◽  
Approach To Learning ◽  
Trace Data ◽  
Deep Approach To Learning ◽  
And Performance

The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper focuses on the link between the learning strategies identified in the trace data and student reported approaches to learning. The paper reports on the findings of a study conducted in the scope of an undergraduate engineering course (N=144) that followed a flipped classroom design. The study found that learning strategies extracted from trace data can be interpreted in terms of deep and surface approaches to learning. The detected significant links with self-report measures are with small effect sizes for both the overall deep approach to learning scale and the deep strategy scale. However, there was no observed significance linking the surface approach to learning and surface strategy nor were there significant associations with motivation scales of approaches to learning. The significant effects on academic performance were found, and consistent with the literature that used self-report instruments showing that students who followed a deep approach to learning had a significantly higher performance.

scholarly journals STUDENT'S CONCEPTS OF AND APPROACHES TO LEARNING AND THE RELATIONSHIPS BETWEEN THEM

2020 ◽  
Vol 39 (3) ◽  
pp. 620-632
Author(s):  
Mohammad Ahmad Alkhateeb ◽  
Osamah Abdel Qader Bani Milhem
Keyword(s):  
Qualitative Content Analysis ◽  
Approaches To Learning ◽  
Learning Approaches ◽  
Deep Approach ◽  
Conceptions Of Learning ◽  
Surface Approach ◽  
Approach To Learning ◽  
Deep Approach To Learning ◽  
Study Population ◽  
Surface Approach To Learning

The study attempted to characterize students’ conceptions of learning and approaches to learning and revealing the correlation between the students’ concepts and approaches to learning. The researchers used qualitative content analysis and a descriptive approach. The study population comprised 90 male and female students of the Faculty of Educational Science in the HU University, Jordan, during the 2019/2020 academic year. The quantitative concepts were dominating among students (87.77%), especially the concept of learning as a knowledge increase (33.33%). On the other hand, the qualitative concepts of learning were low (12.22%), especially on the person change (2.22%). In addition, there was an emergence of a new concept of learning outside the traditional concepts, namely learning as exam preparation. The results showed that the deep approach to learning was low, and the surface approach to learning was high. The results further showed a correlation between the quantitative concepts of learning and the surface approach to learning, as well as a correlation between the qualitative concepts of learning the deep approach to learning. Hence, the general conclusion implies that if teachers are to place learners at the heart of the learning process, they must be aware of the concepts of learning and learning approaches of the students.

scholarly journals Components in models of learning: Different operationalisations and relations between components

2013 ◽  
Vol 45 (1) ◽  
pp. 62-85
Author(s):  
Snezana Mirkov
Keyword(s):  
Learning Styles ◽  
Learning Strategies ◽  
Factor Structure ◽  
Approaches To Learning ◽  
Goal Orientations ◽  
Learning Processes ◽  
Constructive Learning ◽  
Approach To Learning ◽  
School Learning

This paper provides the presentation of different operationalisations of components in different models of learning. Special emphasis is on the empirical verifications of relations between components. Starting from the research of congruence between learning motives and strategies, underlying the general model of school learning that comprises different approaches to learning, we have analyzed the empirical verifications of factor structure of instruments containing the scales of motives and learning strategies corresponding to these motives. Considering the problems in the conceptualization of the achievement approach to learning, we have discussed the ways of operational sing the goal orientations and exploring their role in using learning strategies, especially within the model of the regulation of constructive learning processes. This model has served as the basis for researching learning styles that are the combination of a large number of components. Complex relations between the components point to the need for further investigation of the constructs involved in various models. We have discussed the findings and implications of the studies of relations between the components involved in different models, especially between learning motives/goals and learning strategies. We have analyzed the role of regulation in the learning process, whose elaboration, as indicated by empirical findings, can contribute to a more precise operationalisation of certain learning components.

Exploring college students’ conceptions of learning computer science: a draw-a-picture technique study

2020 ◽  
pp. 1-23
Author(s):  
Zhen Xu ◽  
Albert D. Ritzhaupt ◽  
Karthikeyan Umapathy ◽  
Yang Ning ◽  
Chin-Chung Tsai
Keyword(s):  
College Students ◽  
Computer Science ◽  
Conceptions Of Learning

scholarly journals Metacognitive Knowledge and Skills in Students with Deep Approach to Learning. Evidence from Mathematical Problem Solving // Conocimiento y habilidades metacognitivas en estudiantes con un enfoque profundo de aprendizaje. Evidencias en la resolución de problemas matemáticos

2015 ◽  
Vol 20 (2) ◽  
pp. 209-226 ◽  
Author(s):  
Trinidad García ◽  
Marisol Cueli ◽  
Celestino Rodríguez ◽  
Jennifer Krawec ◽  
Paloma González-Castro
Keyword(s):  
Learning Strategies ◽  
Mathematical Problem ◽  
Approaches To Learning ◽  
Metacognitive Knowledge ◽  
Metacognitive Skills ◽  
Deep Approach ◽  
Process Measures ◽  
Approach To Learning ◽  
Knowledge And Skills ◽  
Deep Approach To Learning

Student approaches to learning and metacognitive strategies are two important conditioning factors in solving mathematical problems. The evidence suggests that it is the deep approach to learning which leads to student success in such tasks. The present study focused on analyzing the differences in metacognitive knowledge and skills in a sample of 524 fifth and sixth grade students divided into three groups based on their different levels of use of a deep approach (241= low; 152= medium; and 131= high). Metacognitive knowledge was assessed using the Learning Strategies Knowledge Questionnaire, while evidence about metacognitive skills was gathered by means of process measures (Triple Tasks Procedure) during students’ solving of two mathematical word problems. Statistically significant differences in metacognitive knowledge were found among groups while differences in metacognitive skills were only found in the second task, with a low effect size.

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