The objective of interdisciplinary education is to teach multiple domains, so that the students can solve integrative problems that require knowledge and skills from these domains. Interdisciplinary programs, in which computer science is combined with other domains, range from bioinformatics to computational linguistics and data science. Data science is an interdisciplinary field that integrates computer science, mathematics, statistics, and the domain knowledge of the data involved. It is a relevant research skill for many disciplines, including social sciences. Specifically, graduate psychology students who wish to learn data science have already mastered the psychology domain knowledge and their statistics background is advanced. They lack, however, the computer science knowledge required for meaningfully handling data science. The problem is, however, that there is no CS1 course that fits graduate psychology students who study data science: On the one hand, the current CS1 courses do not suit social science students; on the other hand, CS1 courses for non-majors usually do not meet the required level of computer science needed for data science. Accordingly, to close this gap, we designed a new CS1 course – an interdisciplinary Introduction to Computer Science for Psychological Science course – that a) suits social science students and b) meets the required computer science level for data science. We evaluated the course by examining students’ learning outcomes, reflections, and responses on questionnaires. Based on the data analysis according to the self-determination motivation theory, it is concluded that the course design enabled to achieve the course’s targets.

Gender imbalance in STEM (Science, Technology, Engineering and Mathematics) studies and occupations is a well-known phenomenon with a large body of research that tries to explain it and offer remedial interventions. Data science is a new and interdisciplinary STEM-oriented domain, integrating knowledge and skills from computer science, mathematics, and statistics with an application domain, from which the data draw their context and meaning. Data science applications are relevant for various domains, and therefore, a variety of populations are increasingly attracted to learning it. Addressing the theme of the conference, i.e., “Women in Engineering”, in this paper we describe a data science workshop for social sciences and digital humanities researchers. A significant majority (83%) of the participants of this workshop self-identified as women. This gender proportion, the opposite of that prevailing in STEM studies, led us to examine the workshop from a gender perspective. Our results indicate that the women participating in the data science workshop perceived it as an opportunity to acquire research tools rather than programming tools. We suggest that framing the workshop as a research tool workshop and not as a programming workshop reduced prevalent gender barriers in STEM, encouraging a majority of women researchers to participate. In this paper, we elaborate on the participants' perceptions about data science and programming and analyze them based on three theoretical perspectives: expectancy value theory, the interdisciplinary perspective, and the epistemological perspective. Keywords-data science, data science education, gender balance, interdisciplinary of data science.

Data science in an emerging interdisciplinary field integrating knowledge and skills from computer science, statistics, and an application domain. The contribution of computer science education to the field of data science education is therefore evident. As computer science educators, however, we can benefit from adapting concepts and methods of data science education as well. For example, working with real data sets, a common practice in data science education, can benefit computer science students, giving them better sense of real-life problems; Statistical thinking, a fundamental thinking skill for data scientists, can enhance computational thinking as a real-life problem-solving skill. This BOF will provide a platform to discuss both the possible benefits computer science educators can earn from the emerging field of data science education and practical pedagogical methods to achieve these benefits.

Machine learning is a fast-growing field with various applications in artificial intelligence and data science. Recently, a new machine learning program have been integrated into the Israeli high school computer science curriculum and thus we added a new machine learning module to the Methods of Teaching Computer Science (MTCS) course, which is part of the teachers' preparation program. This machine learning module provides us a unique opportunity to teach both pedagogy and content with the same subject matter. After teaching the basics of machine learning, we asked the students to find similarities between human learning theories and machine learning algorithms. Students identified several interesting parallels: (a) Supervised learning is similar to behavioral learning as the machine learns to connect training examples (stimuli) with labels (behavior). Also, the learning is based on minimizing error (punishment) function, (b) Reinforcement learning is similar to behavioral learning as learning is based on feedback from the environment, (c) Constructivism can be identified in the iterative convergence of the algorithm; the inner model improves each iteration based on the current knowledge, and (d) Social learning is reflected in clustering as each cluster affects the learning of the other clusters. In our talk, we present the idea that computational mental models may be used to reinforce pedagogical mental models and vice versa.

Interdisciplinary perspective on real-life problems is an important skill for 21st century engineers and should be a part of their education. Project-based learning (PBL) is a well-known pedagogical tool for interdisciplinary education. In order to successfully accomplish interdisciplinary learning, students must have sufficient knowledge in each of the separate disciplines. Use of cross-disciplinary teams, for example, is one way to make sure a project team includes specialists in all required disciplines. This, however, is not always the case. In many cases, there are significant knowledge gaps within the project team in one or more of the project domains. Specifically, in our case, fourth-year electrical engineering students are working in pairs on biomedical signal processing projects, which are evidently interdisciplinary in nature. The teams, however, are homogeneous and the students lack the essential medical expertise required to achieve solutions applicable by physicians. Furthermore, students tend to acknowledge this gap only in the advanced phases of the project, and so critical phases, such as goal setting and planning, are performed without the required knowledge. In this article, we present (a) data that supports the existence of this knowledge gap and its effect on students; (b) an intervention program that exposes students to both the required domain knowledge and its importance to their work; and (c) initial data that supports the success of this intervention program in bridging these gaps.

Data science is a new field of research focuses on extracting knowledge and value from data. The radical growth in recent years in the availability of data and of the computational resources required to process them, has led to a corresponding increase in demand for data scientists. As a result, new data science education programs are opening at a growing rate.