Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modeling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students’ performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performance and identify the critical areas that need significant improvement.
Programs in bioinformatics, offered in many academic institutes, are assumed to expand women’s representation in computer science (CS). Women’s enrolment in these programs is high; Our questions are: Do these programs attract different women from those attracted to CS programs? What factors underlie women’s decision to enroll in bioinformatics programs? How do these factors differ from those of women who choose CS, if at all? What career opportunities do these women anticipate and pursue? Using questionnaires and interviews, we found a statistically significant difference between the factors that motivate women to choose bioinformatics and others to study CS. Many bioinformatics students did not consider CS as an alternative. Post-facto they learned to love computing, albeit with a biology-oriented purpose. “Computing with purpose” underlies many participants’ pursuit of careers in research, CS, and bio-tech. We thus conclude that bioinformatics programs do indeed expand women’s representation in CS.
This study aims to explore the usability of the virtual robotics programming curriculum (VRP-C) for robotics programming teaching. Pre-service computer science (CS) teachers were trained for robotics programming teaching by using VRP-C in a scientific education activity. After training, views of the participants were revealed by using a scale and an evaluation form consisting of open-ended questions. Results show that VRP-C is compatible with the curriculum for robotics programming teaching in schools, and pre-service CS teachers tend to use VRP-C in their courses. They think that VRP-C will be beneficial for robotics programming teaching in terms of content, functionality, and cost. Compatibility, visual design, feedback, time management, fiction, gamification, and cost are the characteristics that increase the usability of VRP-C. VRP-C can be used as an online tool for robotics programming training due to the necessity of transition to distance education because of the COVID-19 pandemic.
In a previous publication we examined the connections between high-school computer science (CS) and computing higher education. The results were promising—students who were exposed to computing in high school were more likely to take one of the computing disciplines. However, these correlations were not necessarily causal. Possibly those students who took CS courses, and especially high-level CS courses in high school, were already a priori inclined to pursue computing education. This uncertainty led us to pursue the current research. We aimed at finding those factors that induced students to choose CS at high school and later at higher-education institutes. We present quantitative findings obtained from analyzing freshmen computing students' responses to a designated questionnaire. The findings show that not only did high-school CS studies have a major impact on students’ choice whether to study computing in higher education—it may have also improved their view of the discipline.
Computing as a discipline has common roots with mathematics and written languages, and computing as a way of thinking and handling has been integral to human culture since ever. This is not only a reasonable argument for convincing society to consider informatics as one of the very fundamental pillars of education, but it also puts the potential contributions of teaching informatics in schools into the correct perspective in the context of science and humanities. Many European countries are switching from teaching information technologies to informatics education during the current second decade of this century. Informatics curriculum is becoming a central part of school education. We explain and design a way of developing informatics curriculum that offer the critical competences new generations need to survive and thrive in todays’ knowledge society and will allow them to contribute to the future development of society. These competences also strongly support the development of their intellectual potential and creativity. Our design of informatics curriculum takes into account the interaction with other scientific disciplines as well with the subject didactics, pedagogy and psychology. The starting point is merging constructionism and critical thinking. Constructionism with its “learning by doing” and “learning by getting things to work” enables designing a teaching process in which students acquire knowledge by creating products, analysing the properties and the functionality of their own products, and finally derive motivation to improve these products. Critical thinking asks us not to teach products of science and technology and their application, but to teach the creative process of their development. To implement this approach, we use the historical method allowing the students to learn by productive failures in the process of searching for a solution. To organize the process of learning and make the different steps available to the appropriate age groups we take into account the cognitive dimensions of the revised taxonomy of Bloom. To illustrate how the combination of all these concepts works we present a detailed curriculum for algorithm design, programming, robotics, and communication in networks.
The paper discusses a certain type of competitions based on distance interaction of a participant with simulation models of concepts from discrete mathematics and computer science. One of them is the “Construct, Test, Explore” (CTE) competition, developed by the authors, the other is the Olympiad in Discrete Mathematics and Theoretical Informatics (DM&TI). The tasks presented in this paper are generally devoted to the concept of a graph isomorphism. Most of the tasks are verified automatically.
Computer science concepts have an important part in other subjects and thinking computationally is being recognized as an important skill for everyone, which leads to the increasing interest in developing computational thinking (CT) as early as at the comprehensive school level. Therefore, research is needed to have a common understanding of CT skills and develop a model to describe the dimensions of CT. Through a systematic literature review, using the EBSCO Discovery Service and the ACM Digital Library search, this paper presents an overview of the dimensions of CT defined in scientific papers. A model for developing CT skills in three stages is proposed: i) defining the problem, ii) solving the problem, and iii) analyzing the solution. Those three stages consist of ten CT skills: problem formulation, abstraction, problem reformulation, decomposition, data collection and analysis, algorithmic design, parallelization and iteration, automation, generalization, and evaluation.
Context: concept Maps (CMs) enable the creation of a schematic representation of a domain knowledge. For this reason, CMs have been applied in different research areas, including Computer Science. Objective: the objective of this paper is to present the results of a systematic mapping study conducted to collect and evaluate existing research on CMs initiatives in Computer Science. Method: the mapping study was performed by searching five electronic databases. We also performed backward snowballing and manual search to find publications of researchers and research groups that accomplished these studies. Results: from the mapping study, we identified 108 studies addressing CMs initiatives in different subareas of Computer Science that were reviewed to extract relevant information to answer a set of research questions. The mapping shows an increasing interest in the topic in recent years and it has been extensively investigated due to support in teaching and learning. Conclusions: based on our results we conclude that the use of CMs as an educational tool has been widely accepted in Computer Science.
Research on the effectiveness of introductory programming environments often relies on post-test measures and attitudinal surveys to support its claims; but such instruments lack the ability to identify any explanatory mechanisms that can account for the results. This paper reports on a study designed to address this issue. Using Noss and Hoyles' constructs of webbing and situated abstractions, we analyze programming novices playing a program-to-play constructionist video game to identify how features of introductory programming languages, the environments in which they are situated, and the challenges learners work to accomplish, collectively affect novices' emerging understanding of programming concepts. Our analysis shows that novices develop the ability to use programming concepts by building on the suite of resources provided as they interact with the computational context of the learning environment. In taking this approach, we contribute to computer science education design literature by advancing our understanding of the relationship between rich, complex introductory programming environments and the learning experiences they promote.
We present an overview of the nature of academic dishonesty with respect to computer science coursework. We discuss the efficacy of various policies for collaboration with regard to student education, and we consider a number of strategies for mitigating dishonest behaviour on computer science coursework by addressing some common causes. Computer science coursework is somewhat unique, in that there often exist ideal solutions for problems, and work may be shared and copied with very little effort. We discuss the idiosyncratic nature of how collaboration, collusion and plagiarism are defined and perceived by students, instructors and administration. After considering some of the common reasons for dishonest behaviour among students, we look at some methods that have been suggested for mitigating them. Finally, we propose several ideas for improving computer science courses in this context. We suggest emphasizing the intended learning outcomes of each assignment, providing tutorial sessions to facilitate acceptable collaboration, delivering quizzes related to assignment content after each assignment is submitted, and clarifying the boundary between collaboration and collusion in the context of each course. While this discussion is directed at the computer science community, much may apply to other disciplines as well, particularly those with a similar nature such as engineering, other sciences, or mathematics.