Computing education theories : what are they and how are they used?

In order to mature as a research field, computing education research (CER) seeks to build a better theoretical understanding of how students learn computing concepts and processes. Progress in this area depends on the development of computing-specific theories of learning to complement the general theoretical understanding of learning processes. In this paper we analyze the CER literature in three central publication venues -- ICER, ACM Transactions of Computing Education, and Computer Science Education -- over the period 2005--2015. Our findings identify new theoretical constructs of learning computing that have been published, and the research approaches that have been used in formulating these constructs. We identify 65 novel theoretical constructs in areas such as learning/understanding, learning behaviour/strategies, study choice/orientation, and performance/progression/retention. The most common research methods used to devise new constructs include grounded theory, phenomenography, and various statistical models. We further analyze how a number of these constructs, which arose in computing education, have been used in subsequent research, and present several examples to illustrate how theoretical constructs can guide and enrich further research. We discuss the implications for the whole field

Engineering Education Research

This chapter describes several aspects of engineering education research with an emphasis on how they might relate to computing education research. We briefly summarize the history of engineering education as a scholarly field, and we describe the current structures that support engineering education research: academic departments, scholarly journals, annual conferences, and professional societies. We identify the theories that often inform engineering education research studies, including theories of cognition, motivation, and identity. We explain how quantitative, qualitative, and mixed methods have been used. We summarize the results of an illustrative selection of empirical studies across a broad range of topics, including instructional methods, student development, faculty teaching practices, diversity, and assessment. Finally, we outline some similarities and differences between computing education research and engineering education research. Engineering education research has a longer history of research in professional development and assessment but an arguably shorter history in pre-college education and less international integration than computing education research

Computer Science Concept Inventories: Past and Future

Concept Inventories (CIs) are assessments designed to measure student learning of core concepts. CIs have become well known for their major impact on pedagogical techniques in other sciences, especially physics. Presently, there are no widely used, validated CIs for computer science. However, considerable groundwork has been performed in the form of identifying core concepts, analyzing student misconceptions, and developing CI assessment questions. Although much of the work has been focused on CS1 and a CI has been developed for digital logic, some preliminary work on CIs is underway for other courses. This literature review examines CI work in other STEM disciplines, discusses the preliminary development of CIs in computer science, and outlines related research in computer science education that contributes to CI development

An Assessment of Conceptual Understanding of Metabolic Pathways

Researchers have previously studied misconceptions of biochemistry topics such as photosynthesis, protein structure, and ATP-production. However, no studies have reported on students’ misconceptions regarding major metabolic pathways. Since learning metabolism builds on students’ prior knowledge, new material being learned will be affected by the presence of any misconceptions. Some of these misconceptions will be robust and thus hard to be replaced by the correct concepts. Thus, if students are to learn new material, these misconceptions must be diminished. This dissertation focused on the origins of these misconceptions, investigated what misconceptions students had on metabolism, and why students developed misconceptions instead of proper scientific conceptions. The ultimate goal of this study was to help students improve their conceptual understanding of general chemistry concepts that impact metabolic pathways by developing a video that targeted most of their misconceptions. This video depicted some metabolic reactions at the molecular level. Students’ misconceptions were first identified; based on them, an instructional intervention was designed to help students develop better, well-constructed conceptual schema (posttest). Evidence presented in the sample suggested the use of multimedia in helping students understand biochemistry was effective. An exploratory mixed methods design was used as a first stage to pilot any misconceptions among biochemistry students. A case study was conducted to investigate if graduate chemistry major students had any misconceptions on metabolism (n = 6). Based on the misconceptions found, the video was created to help undergraduate students develop a proper scientific understanding of the main concept targeted by this dissertation, which was chemical potential energy. The first phase of this research included quantitative validation of the instrument used (n = 45) and the second phase involved a phenomenological study where 11 graduate non-chemistry major students volunteered to participate. Many misconceptions were revealed by this study and most of them seemed to be prevalent and quite persistent

Understanding and Addressing Misconceptions in Introductory Programming: A Data-Driven Approach

With the expansion of computer science (CS) education, CS teachers in K-12 schools should be cognizant of student misconceptions and be prepared to help students establish accurate understanding of computer science and programming. This exploratory design-based research (DBR) study implemented a data-driven approach to identify secondary school students’ misconceptions using both their compilation and test errors and provide targeted feedback to promote students’ conceptual change in introductory programming. Research subjects were two groups of high school students enrolled in two sections of a Java-based programming course in a 2017 summer residential program for gifted and talented students. This study consisted of two stages. In the first stage, students of group 1 took the introductory programming class and used an automated learning system, Mulberry, which collected data on student problem-solving attempts. Data analysis was conducted to identify common programming errors students demonstrated in their programs and relevant misconceptions. In the second stage, targeted feedback to address these misconceptions was designed using principles from conceptual change and feedback theories and added to Mulberry. When students of group 2 took the same introductory programming class and solved programming problems in Mulberry, they received the targeted feedback to address their misconceptions. Data analysis was conducted to assess how the feedback affected the evolution of students’ (mis)conceptions. Using students’ erroneous solutions, 55 distinct compilation errors were identified, and 15 of them were categorized as common ones. The 15 common compilation errors accounted for 92% of all compilation errors. Based on the 15 common compilation errors, three underlying student misconceptions were identified, including deficient knowledge of fundamental Java program structure, misunderstandings of Java expressions, and confusion about Java variables. In addition, 10 common test errors were identified based on nine difficult problems. The results showed that 54% of all test errors were related to the difficult problems, and the 10 common test errors accounted for 39% of all test errors of the difficult problems. Four common student misconceptions were identified based on the 10 common test errors, including misunderstandings of Java input, misunderstandings of Java output, confusion about Java operators, and forgetting to consider special cases. Both quantitative and qualitative data analysis were conducted to see whether and how the targeted feedback affected students’ solutions. Quantitative analysis indicated that targeted feedback messages enhanced students’ rates of improving erroneous solutions. Group 2 students showed significantly higher improvement rates in all erroneous solutions and solutions with common errors compared to group 1 students. Within group 2, solutions with targeted feedback messages resulted in significantly higher improvement rates compared to solutions without targeted feedback messages. Results suggest that with targeted feedback messages students were more likely to correct errors in their code. Qualitative analysis of students’ solutions of four selected cases determined that students of group 2, when improving their code, made fewer intermediate incorrect solutions than students in group 1. The targeted feedback messages appear to have helped to promote conceptual change. The results of this study suggest that a data-driven approach to understanding and addressing student misconceptions, which is using student data in automated assessment systems, has the potential to improve students’ learning of programming and may help teachers build better understanding of their students’ common misconceptions and develop their pedagogical content knowledge (PCK). The use of automated assessment systems with misconception identification components may be helpful in pre-college introductory programming courses and so is encouraged as K-12 CS education expands. Researchers and developers of automated assessment systems should develop components that support identifying common student misconceptions using both compilation and non-compilation errors. Future research should continue to investigate the use of targeted feedback in automated assessment systems to address students’ misconceptions and promote conceptual change in computer science education

Experiential learning in control systems laboratories and engineering project management

Experiential learning is a process by which a student creates knowledge through the insights gained from an experience. Kolb's model of experiential learning is a cycle of four modes: (1) concrete experience, (2) reflective observation, (3) abstract conceptualization, and (4) active experimentation. His model is used in each of the three studies presented in this dissertation. Laboratories are a popular way to apply the experiential learning modes in STEM courses. Laboratory kits allow students to take home laboratory equipment to complete experiments on their own time. Although students like laboratory kits, no previous studies compared student learning outcomes on assignments using laboratory kits with existing laboratory equipment. In this study, we examined the similarities and differences between the experiences of students who used a portable laboratory kit and students who used the traditional equipment. During the 2014-2015 academic year, we conducted a quasi-experiment to compare students' achievement of learning outcomes and their experiences in the instructional laboratory for an introductory control systems course. Half of the laboratory sections in each semester used the existing equipment, while the other sections used a new kit. We collected both quantitative data and qualitative data. We did not identify any major differences in the student experience based on the equipment they used. Course objectives, like research objectives and product requirements, help provide clarity and direction for faculty and students. Unfortunately, course and laboratory objectives are not always clearly stated. Without a clear set of objectives, it can be hard to design a learning experience and determine whether students are achieving the intended outcomes of the course or laboratory. In this study, I identified a common set of laboratory objectives, concepts, and components of a laboratory apparatus for undergraduate control systems laboratories. During the summer of 2015, a panel of 40 control systems faculty members, from a variety of institutions, completed a multi-round Delphi survey in order to bring them toward consensus on the common aspects of their laboratories. The following winter, 45 additional faculty members and practitioners from the control systems community completed a follow-up survey to gather feedback on the results of the Delphi survey. During the Delphi study, the panelists identified 15 laboratory objectives, 26 concepts, and 15 components that were common in their laboratories. Then in both the Delphi survey and follow-up survey each participant rated the importance of each of these items. While the average ratings differed slightly between the two groups, the order of each set of items was compared with two different tests and the order was found to be similar. Some of the common and important learning objectives include connecting theory to what is implemented and observed in the laboratory, designing controllers, and modeling and simulating systems. The most common component in both groups was MathWorks software. Some of the common concepts include block diagrams, stability, and PID control. Defining common aspects of undergraduate control systems laboratories enables common development, detailed comparisons, and simplified adaptation of equipment and experiments between campuses and programs. Throughout an undergraduate program in engineering, there are multiple opportunities for hands-on laboratory experiences that are related to course content. However, a similarly immersive experience for project management graduate students is harder to incorporate for all students in a course at once. This study explores an experiential learning opportunity for graduate students in engineering management or project management programs. The project management students enroll in a project management course. Undergraduate students interested in working on a project with a real customer enroll in a different projects course. Two students from the project management course function as project managers and lead a team of undergraduate students in the second course through a project. I studied how closely the project management experience in these courses aligns with engineering project management in industry. In the spring of 2015, I enrolled in the project management course at a large Midwestern university. I used analytic autoethnography to compare my experiences in the course with my experiences as a project engineer at a large aerospace company. I found that the experience in the course provided an authentic and comprehensive opportunity to practice most of the skills listed in the Project Management Book of Knowledge (an industry standard) as necessary for project managers. Some components of the course that made it successful: I was the project manager for the whole term, I worked with a real client, and the team defined and delivered the project before the end of the semester

Enhancing comprehension in open distance learning computer programming education with visualization

This thesis describes a research project aimed at improving the tracing skills of first-year programming students enrolled for an introductory C++ course at an open distance learning institution by means of a tutorial in the form of a program visualization tool to teach the students to draw variable diagrams. The research was based on the findings from the BRACElet project (Clear, Whalley, Robbins, Philpott, Eckerdal, Laakso & Lister, 2011). A design-based research methodology was followed. To guide the process of developing the tutorial, a framework of 26 guidelines for developing and using visualization tools to teach programming was synthesized from the literature on computing education research CER, educational psychology and computer graphics. Guidelines were supplemented with reasons or explanations for their recommendation and considerations to be taken into account when using a guideline. The framework was enhanced by lessons learnt during the development and testing of the tutorial. The tutorial was tested and refined during two implementation cycles. Both cycles included quantitative and qualitative investigations. All students registered for the introductory module received the tool with their study material. For the quantitative investigations, students completed a questionnaire after using the tutorial. Through the questionnaire biographical data was acquired, the manner in which students used the tutorial and how they experienced using it. The responses to the questionnaires were statistically analysed in combination with respondents’ final marks. The statistical modelling indicated that the students’ biographical properties (a combination of level of programming experience, marks obtained for Mathematics and English in matric and first-time registration for COS1511 or not), had the biggest impact on their final marks by far. During the qualitative investigations students were eye tracked in a Human-Computer Interaction laboratory. The gaze replays in both cycles revealed that students’ reading skills impacted largely on their success, connecting with the findings from the quantitative investigations. Reflections on why the tutorial did not achieve its purpose; and why poor reading skills may have such a strong effect on learning to program, contribute some theoretical understanding as to how novices learn to program.Computer ScienceD. Phil. (Computer Science