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Contribution: This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect bias and is discussed in this article from the cognitive psychology perspective. Background: ML algorithms are common components in electrical engineering, computer science, and data science curricula. Learning ML is challenging not only due to its complex mathematical and algorithmic aspects, but also due to: (1) the complexity of using these algorithms in the context of real-life situations correctly and (2) the comprehension of related social and ethical issues. Research Question: To what extent do students fail to correctly interpret the performance of ML algorithms due to the domain neglect bias and how can this bias be explained? Methodology: A questionnaire containing two questions was designed, in which the participants were asked to compare the performance of ML algorithms in the contexts of different application domains. The questions were designed to trigger the domain neglect bias which in turn, allowed quantification of its prevalence. Findings: One third of the students failed to recognize the outperforming ML algorithm due to the domain neglect bias. Educators should note this bias and develop new pedagogical methods for teaching ML performance in order to mitigate the bias.

Data science is a new field of research that has attracted growing interest in recent years as it focuses on turning raw data into understanding, insight, knowledge, and value. New data science education programs, which are being launched at an increasing rate, are designed for multiple education levels and populations. Machine learning (ML) is an essential element of data science that requires an extensive background in mathematics. Whereas it is possible to teach the principles of ML only as a black box, novice learners might find it difficult to improve an algorithm's performance without a white box understanding of the underlying ML algorithms. In this paper, we suggest a pedagogical method, based on hands-on pen-and-paper tasks, to support white box understanding of ML algorithms for learners who lack the level of mathematics knowledge required for this purpose. Data were collected using a comprehension questionnaire and analyzed according to the process-object theory borrowed from mathematics education research. We present evidence of the effectiveness of this method based on data collected in an introduction-level data science course for graduate psychology students. This population had extensive psychology domain knowledge, as well as an established background in statistics, but had gaps in mathematical and computer science knowledge compared with data science majors. The research contribution is both practical and theoretical. Practically, we present a learning module that supports non-major data science students' white box understanding of ML. Theoretically, we propose a data analysis method to evaluate students' conceptions of ML algorithms.

Aim/Purpose: This paper presents a study about changes in computer science and software engineering students' perceptions of their soft skills during their progress through the Computer Science Soft Skills course. Background: Soft skills are often associated with a person's social, emotional and cognitive capabilities. Soft skills are increasingly sought out and are well recognized by employers alongside standard qualifications. Therefore, high importance is attributed to soft skills in computer science and software engineering education. Methodology: Content analysis was applied to interpret, categorize and code statements from students' course assignment answers. Data analysis was performed gradually at the three main stages of the course and by the two students' study populations. Contribution: The paper highlights the variety of (a) soft skills that can be learnt in one course, both on the individual level and on the team level and (b) assignments that can be given to students to increase their awareness and motivation to practice and learn soft skills. Findings: Data analysis revealed the following: (a) five individual soft skills categories, with 95 skills, and five team-related soft skills categories, with 52 skills (in total, the students mentioned 147 soft skills); (b) course assignments and particularly team-based activities elicited student awareness of their individual soft skills, both as strengths and weaknesses; (c) students developed their reflection skills, particularly with respect to team-related soft skills; and (d) significant differences exist between the two groups of students in several categories. Recommendations for Practitioners: It is important to provide undergraduate students with opportunities to integrate soft skills during their training. Establishing a meaningful learning process, such as project-based learning, enables students to apply and develop soft skills when accompanied by reflective thought processes. Recommendations for Researchers: A similar course can be taught and be accompanied by similar analysis of students' learning outcomes, to examine the influence of local culture on the characteristics of soft skills. Impact on Society: Increased awareness of soft skills in scientists and engineers' undergraduate education. University graduates who will strengthen their variety of soft skills in their academic training process and will be more meaningful employees in the workplace and in society. Future Research: Our future research aims (a) to explore additional innovative ways to increase students' learning processes, awareness and practices in relation to soft skills and (b) to research how students' soft skills are developed during the entire undergraduate studies both on the individual level and the team level.

This article describes the construction process and evaluation of the Agile Constructionist Mentoring Methodology (ACMM), a mentoring method for guiding software development projects in the high school. The need for such a methodology has arisen due to the complexity of mentoring software project development in the high school. We introduce the ACMM and suggest a year-long mentoring template that includes the practices required for the actual mentoring process. The evaluation of the ACMM reveals that the methodology addresses each of the challenges teachers cope with during the mentoring process, which were identified in the first phase of the research.

This paper offers a tutoring model its objective is to develop and establish the pedagogical-disciplinary knowledge of prospective computer science teachers with respect to guiding learners in problem-solving processes. The paper presents the tutoring model and the research that accompanied its implementation. The research findings indicate that the main objectives of the tutoring model were accomplished. Specifically, the prospective teachers changed their perspective vis a vis the teaching process, are aware of the importance of identifying learner difficulties and emphasizing this in their teaching process, emphasize problem-solving processes and the imparting thereof, have adopted reflective processes of thinking, learning and teaching and encourage these processes among the learners as well, have reinforced their own self-confidence regarding their ability and place in the disciplinary teaching process, and appreciate the contribution of the tutoring model to their training as future teachers. In general, the tutoring model has the potential to foster the skills of the prospective CS teachers on three levels: the pedagogical-disciplinary professional skills, the pedagogical professional skills and the disciplinary knowledge. (Contains 13 tables.)

This paper focuses on challenges in mentoring software development projects in the high school and analyzes difficulties encountered by Computer Science teachers in the mentoring process according to Shulman's Teacher Knowledge Base Model. The main difficulties that emerged from the data analysis belong to the following knowledge sources of Shulman's model: Content knowledge, pedagogical content knowledge (PCK) and knowledge of learners and their characteristics. We suggest that the complexity of the mentoring process of software project development in the high school results mainly from the fact that the process involves a variety of knowledge types, as well as management activities and pedagogical aspects. This fact leads us to conclude that a mentoring methodology should be defined for this complex task, similar to software project development methodologies applied in the industry. We summarize with a discussion of the nature of such a methodology. (Contains 2 tables and 1 note.)

This paper discusses the role of reduction in computer science and describes a study on undergraduate students' perception of the concept of reduction. Specifically, based on an analysis of students answers to questions addressing different computer science topics, we present several findings regarding the ways in which undergraduate students conceive of and apply reduction. These findings can be interpreted within the framework of the tension that exists between the following two factors: The need to think in terms of high levels of abstraction, on one hand, and the fact that reduction introduces a new approach to be used in problem-solving situations, on the other. In addition the paper suggests several teaching applications.

This article focuses on the similarities and differences in the academic education of software engineers and architects. The rationale for this work stems from our observation, each from the perspective of her or his own discipline, that these two professional design and development processes share some similarities. A pilot study was performed, in which 24 practitioners (12 software engineers and 12 architects) were interviewed concerning the main characteristics of the two professions. The characteristics were classified according to four categories: discipline-related characteristics, professional skills, characteristics of typical problems and processes, and characteristics of a typical product. Data analysis indicates that according to the practitioners, while the professional skills required of professionals in the two disciplines are mostly common, the difference between the two disciplines is reflected most significantly in the features of the product created in the two professional processes. Based on this observation, some conclusions are outlined with respect to the academic education in the two fields.

This article presents a study that aims at constructing a teaching framework for software development methods in higher education. The research field is a capstone project-based course, offered by the Technion's Department of Computer Science, in which Extreme Programming is introduced. The research paradigm is an Action Research that involves cycles of data collection, examination, evaluation, and application of results. The research uses several research tools for data gathering, as well as several research methods for data interpretation. The article describes in detail the research background, the research method, and the gradual emergence process of a framework for teaching software development methods. As part of the comprehensive teaching framework, a set of measures is developed to assess, monitor, and improve the teaching and the actual process of software development projects. (Contains 1 note, 7 tables and 1 figure.)

This article presents research on students' understanding of basic concepts in Graph Theory. Students' understanding is analyzed through the lens of the theoretical framework of reducing abstraction (Hazzan, 1999). As it turns out, in spite of the relative simplicity of the concepts that are introduced in the introductory part of a traditional Graph Theory course, some students exhibit ways of thinking that indicate a reduction of the level of abstraction. The importance of this study is derived from the importance of graph algorithms in any computer science curriculum and the centrality of the concept of abstraction in computer science education. (Contains 7 figures.)

The research presented in this article deals with the difficulties and mental processes involved in the definition, implementation, and use of abstract data types encountered by 12th grade advanced-level computer science students. Research findings are interpreted within the theoretical framework of "reducing abstraction" [Hazzan 1999]. The article describes the research setting and findings and concludes with some pedagogical implementations.

This article offers a tutoring model with the objective of developing and establishing the pedagogical-disciplinary knowledge of prospective computer science teachers with respect to guiding learners in problem-solving processes. The model focuses on the tutor, who is a prospective computer science (CS) teacher. During the tutoring process, tutor-students, who have already acquired CS knowledge, support the learning processes of novice CS students. The article presents the tutoring model and the evaluation research that accompanied its implementation. The research findings indicate that the main objectives were accomplished. The prospective teachers changed their perspective vis a vis the teaching process; became aware of the importance of identifying learner difficulties and emphasizing this in their teaching process; emphasized problem-solving processes and their impact; adopted reflective processes of thinking and encouraged these processes among the learners as well; reinforced their own self-confidence regarding their ability and place in the disciplinary teaching process; and came to appreciate the contribution of the tutoring model to their training as future teachers. Accordingly, we conclude that the proposed model has the potential to foster the prospective CS teachers' skills on three levels: pedagogical-disciplinary professional skills, pedagogical professional skills and disciplinary knowledge. Further, we suggest that the tutoring model can be applied in other disciplines in which problem solving is an inherent part of the discipline. [ABSTRACT FROM AUTHOR]

Worldwide surveys indicate that the number of women studying undergraduate-level Computer Science (CS) is constantly decreasing. Based on data collected in Israel, significant differences were found in the percentages of female high school students studying advanced-level CS among different sectors. More specifically, while the percentage of female students among high school students studying advanced-level CS is about 50% for the Arab minority sector, the percentage of female students among students studying CS at the same level among the Jewish majority sector is only about 25%. This situation was investigated by using both qualitative and quantitative research tools. This article analyzes the situation within the model of "achievement-related choices" developed by Eccles and her colleagues. [ABSTRACT FROM AUTHOR]

This article presents research on students' understanding of basic concepts in Graph Theory. Students' understanding is analyzed through the lens of the theoretical framework of reducing abstraction (Hazzan, 1999). As it turns out, in spite of the relative simplicity of the concepts that are introduced in the introductory part of a traditional Graph Theory course, some students exhibit ways of thinking that indicate a reduction of the level of abstraction. The importance of this study is derived from the importance of graph algorithms in any computer science curriculum and the centrality of the concept of abstraction in computer science education. [ABSTRACT FROM AUTHOR]

The author proposes the inclusion of human aspects in university curricula on software engineering (SE). One of the reasons why human aspects are not integrated in SE is lack of general engineering education among computer science (CS) graduates. She also suggests qualitative research for human aspects of SE rather than quantitative one used as research method in SE. She urges the software industry to consider human aspects in SE.