First-year Computer Science Research Articles

Learning from Designing a Board Game for Policy Thinking in Computer Science

Technology does not exist in a vacuum, but is influenced by society and at the same time shaping it. In order to educate future computer scientists to be able to assess and create technology in a conscious and responsible manner, we provide first-year Computer Science (CS) students with an introductory course on different ways of thinking about CS by offering them a transdisciplinary view of the field. As we continuously strive to use engaging teaching methods to foster long-lasting interest for the topics of the course, as well as enhancing students’ learning experiences, we have introduced a unique game-based learning pipeline: In a game design course, advanced CS students design non-digital games that address various topics from the introductory course. These games are later played by first-year CS students, who are reflecting on the content and their experience as a part of their course assignments. In order to learn from the first iteration of this process, we analyse one of the games created, in particular its design and the educational objectives intended by the game designers, including the students’ feedback. We found that the game was perceived to be good in achieving learning outcomes; however, it scored even higher in terms of being an interesting and entertaining game. Overall, we conclude that the game was successful in creating an engaging learning experience, demonstrating the potential for game-based learning with non-digital games. This case study also highlights the importance of creating a suitable environment for game-based learning approaches to enhance the educational effects and utilise individual strengths of educational games, providing inspiration and groundwork for future research.

An Innovative STEAM-Based Method for Teaching Cycloidal Curves in Engineering Higher Education

Robotics offers innovative possibilities at all levels of education, which should be considered when planning the teaching process for certain engineering mathematics topics in higher education. This paper introduces a new teaching–learning technique that utilizes STEAM-based methods to explore cycloidal curves for Computer Science Engineering BSc students. Traditional frontal teaching has been enhanced with methods addressing the generational needs of students, including problem-based learning, STEAM integration, and project-based learning. We developed a methodological model that merges traditional teaching advantages with practical modern approaches suitable for Generation Z. The four-stage model for learning cycloidal curves employs various didactical approaches, utilizing different learning techniques at each stage to create an engaging and effective learning experience. A vital component of this model is the use of Desmos dynamic geometry software to create animations alongside educational robotics to aid visualization. We conducted quantitative studies with 98 first-year Computer Science Engineering students using a quasi-experimental research design to evaluate the new teaching technique’s effectiveness. Results from the Mann–Whitney U test indicated that the experimental group significantly outperformed the control group. Additionally, the Kruskal–Wallis test confirmed that the four-stage model for learning cycloidal curves enhances learning achievement for all students, regardless of their prior knowledge.

Fostering student competencies and perceptions through artificial intelligence of things educational platform

The growing demand for artificial intelligence (AI) skills across various sectors has enhanced AI-focused careers and shaped academic exploration in educational institutions. These institutions have been actively developing teaching methods that enhance practical AI applications, particularly through integrating AI with the Internet of Things (IoT), leading to the emergence of the Artificial Intelligence of Things (AIoT). This convergence promises significant advancements in AI education, addressing gaps in structured learning methods for AIoT. This study explored AIoT's application in Smart Farming (SF) and its potential to enrich AI education and sectoral advancements. The AIoT platform was designed for SF simulations, integrating environmental sensing, AI processing, and user-friendly outputs. This platform was implemented with 40 first-year computer science university students in Thailand using a one-group pre-posttest design. This approach transformed theoretical AI concepts into experiential learning through interactive activities, demonstrating AIoT's capability to increase AI conceptual understanding, trigger AI competencies, and promote positive learning perceptions. Therefore, this study presented the results as indicative of the AIoT platform's potential benefits, emphasizing the need for further robust experimental research. This study contributes to educational technology discussions by suggesting improvements in AIoT platform effectiveness and highlighting areas for future investigation.

Research on the impact of the socio-educational environment on the academic performance of college students: the mediating role of study motivation.

Enhancing the academic performance (AP) of college students can contribute to the overall scientific literacy among this population, thereby fostering societal progress. The study investigates the correlation between college students' AP and the socio-educational environment (SEE, including family, roommates, and teachers), study motivation (SM, including self-efficacy and study behaviors). Based on the research findings, recommendations are offered to students, educators, and school administrators. Utilizing a stratified sampling approach, data was collected by selecting a sample of 330 first-year computer science students from a specific local university in Hebei Province, China. Data will be collected through a hierarchical sampling method. Using correlation analysis, difference analysis, and structural equation modeling (SEM) as data analysis methods. The data passed reliability and validity analysis (Cronbach's Alpha = 0.88, KMO = 0.88, χ2/df = 1.49RMSEA = 0.04). The independent sample T-test results showed that female students had higher academic performance than male students (p < 0.001), and there was no significant difference in academic performance between students from single parent or orphan families and students from normal families (p = 0.14), from non urban areas and from urban areas (p = 0.67). The results of the mediating effect analysis indicate that SM exerts complete mediation in the association between SEE and AP, with a mediating effect value of 0.18. The educational disparity between urban and rural areas in China is gradually narrowing. Support policies for students from impoverished families in higher education institutions are showing initial effectiveness. The conducive learning environment and educational atmosphere for students can indirectly influence their psychological state, thus impacting their academic performance during their university years.

Evaluating the Impact of Serious Games on Study Skills and Habits

Learning is a constructive process that requires dedication and motivation. Learning games can be used to en-gage learners, by providing opportunities to apply knowledge in a safe and fun environment. We created a peer-quizzing game that allows students to quiz each other on the course material and to playfully engage in good study habits. We set out to explore if learners with better learning skills and habits would be more en-gaged in the game in the context of a first-year computer science university class. We used the "Study Skills and Habits Questionnaire" to study the relationship between study skills and habits (especially time management, concentration, goal setting, and comprehension) and engagement in a competitive version of a peer-quizzing game including a leaderboard. We collected and analysed gameplay data of the students (n=34), such as creating quiz questions and solving the questions created by their peers. The results of the data analysis showed a moderate positive correlation between study skills and habits (time management, concentration, goal setting, and comprehension) and the number of questions answered and a moderate negative correlation with the number of questions created in the game. However, the regression model was not statistically significant in explaining the variation in the dependent variable (questions created). Among the individual predictor variables, only goal setting had a statistically significant positive effect on the number of questions created. Other variables, such as time management, concentration, and comprehension, did not show significant effects on the number of questions created by students.

Models of Data Structures in Educational Visualizations for Supporting Teaching and Learning Algorithms and Computer Programming

Teaching and learning computer programming is challenging for many undergraduate first-year computer science students. During introductory programming courses, novice programmers need to learn some basic algorithms, gain algorithmic thinking, improve their logical and problem-solving thinking skills, and learn data types, data structures, and the syntax of the chosen programming language. In literature, we can find various methods of teaching programming that can motivate students and reduce students’ cognitive load during the learning process of computer programming, e.g., using robotic kits, microcontrollers, microworld environments, virtual worlds, serious games, interactive animations, and visualizations. In this paper, we focus mainly on algorithm visualizations, especially on the different models of data structures that can be effectively used in educational visualizations. First, we show how a vector (one-dimensional array), a matrix (two-dimensional array), a singly linked list, and a graph can be represented by various models. Next, we also demonstrate some examples of interactive educational algorithm animations for teaching and learning elementary algorithms and some sorting algorithms, e.g., swapping two variables, summing elements of the array, mirroring the array, searching the minimum or maximum of the array, searching the index of minimum or maximum of the array, sorting elements of thearray using simple exchange sort, bubblesort, insertion sort, minsort, maxsort, quicksort, or mergesort. Finally, in the last part of the paper, we summarize our experiences in teaching algorithmization and computer programming using algorithm animations and visualizations and draw some conclusions.

Self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs

This study investigated the extent to which self-report and digital-trace measures of students’ self-regulated learning in blended course designs align with each other amongst 145 first-year computer science students in a blended “computer systems” course. A self-reported Motivated Strategies for Learning Questionnaire was used to measure students’ self-efficacy, intrinsic motivation, test anxiety, and use of self-regulated learning strategies. Frequencies of interactions with six different online learning activities were digital-trace measures of students’ online learning interactions. Students’ course marks were used to represent their academic performance. SPSS 28 was used to analyse the data. A hierarchical cluster analysis using self-reported measures categorized students as better or poorer self-regulated learners; whereas a hierarchical cluster analysis using digital-trace measures clustered students as more active or less active online learners. One-way ANOVAs showed that: 1) better self-regulated learners had higher frequencies of interactions with three out of six online learning activities than poorer self-regulated learners. 2) More active online learners reported higher self-efficacy, higher intrinsic motivation, and more frequent use of positive self-regulated learning strategies, than less active online learners. Furthermore, a cross-tabulation showed significant (p < .01) but weak association between student clusters identified by self-reported and digital-trace measures, demonstrating self-reported and digital-trace descriptions of students’ self-regulated learning experiences were consistent to a limited extent. To help poorer self-regulated learners improve their learning experiences in blended course designs, teachers may invite better self-regulated learners to share how they approach learning in class.

Usability Evaluation of Imikode Virtual Reality Game to Facilitate Learning of Object-Oriented Programming

Many empirical studies have shown that educational games and recent technologies impact education and increase learning effectiveness, students’ motivation and engagement. The overall aim of this study is to evaluate the usability of Imikode, a virtual reality (VR) game that was developed to introduce the concepts of object-oriented programming to novices. The improved version of the Imikode VR game consists of three features: An artificial intelligence component designed to provide real-time error feedback to users, an intelligent agent that guides and teaches users how to play the game and finally, the integration of multiple game play that gives learners more opportunities to explore the VR environment for greater immersive learning experience. This study adopted a survey approach and recruited first-year computer science students to measure learner satisfaction with educational virtual reality games and examined the correlations among the attributes of the Usefulness, Satisfaction and Ease of Use questionnaire of usage of Imikode. The results showed that the students were satisfied with Imikode and perceived the virtual reality educational game as very useful for learning object-oriented programming concepts. In addition, there was a correlation among the questionnaire variables, which means that researchers can use the instrument for future usability studies in the context. We further proffered some design recommendations for building software tools.

On the computational thinking and diagrammatic reasoning of first-year computer science and engineering students

Computational thinking (CT) and diagrammatic reasoning (DR) are important competencies from the perspective of both Computer Science and Engineering education. CT is often described as a critically important attitude or skill set for all students regardless of the educational program in which they are enrolled. Diagrammatic reasoning is commonly referred to as a student's ability to think logically and solve complex problems. Accordingly, these two competencies are closely related and both skills are parallelly linked to several curriculum subjects (with preponderance in the case of STEM disciplines) during the educational process. Consequently, one might conclude that even without an explicit focus on them, students might develop these abilities latently as they advance with the K-12 current curriculum. We have proposed to test this assumption. In the experiment, 137 first-year students were involved in six different Computer Science and Engineering educational programs. Students were invited to participate in a CT and a DR test. We were particularly interested in possible correlations between the results of the two tests. Our results confirmed that computational thinking and diagrammatic reasoning are closely related abilities. We also found that CT, DR, and students' prior programming experience positively correlate with their first course exam results in Computer Science.

An Educational Setting to improve Students’ Understanding of Fundamental Computer Architecture Concepts

This paper presents an educational setting that attempts to enhance students’ understanding and facilitate students’ linking-inferencing skills. The proposed setting is structured in three stages. The first stage intends to explore students’ prior knowledge. The second stage aims to help students tackle their difficulties and misconceptions and deepen their understanding of the topics under study. This is attempted through individual student engagement in suitably-designed activities and relative feedback. As recorded in previous research, students’ difficulties feedback on the material development. The third stage of the educational setting exploits social interaction to help students reorganize their knowledge of the concepts under study. The web-based application of the proposed educational setting indicated improvement in first-year Computer Science (CS) students’ understanding of fundamental Computer Architecture concepts and progress in students’ linking-inference skills. These results encourage integration in the instructional process of interventions designed according to the proposed setting in order to support and enhance students’ understanding of troublesome concepts and their interrelations.

Combining diagnostic testing and student mentorship to increase engagement and progression of first-year computer science students

ABSTRACT Computer Science (CS) degrees have some of the poorest continuation rates across HE. This study describes an intervention within a diverse CS student cohort to identify students who may be at risk of mathematical academic failure and the success of student mentor-led workshops in enhancing these students’ mathematical ability. Diagnostic screening identified 46% (N = 92) of the cohort with low mathematical knowledge, with prior entry qualification being the only significant (p < 0.0001) reason for this gap. These identified students were invited to attend weekly mathematical workshops, with a positive correlation between attendance and attainment and females more likely to attend. The CS students were satisfied with having the mathematical workshops delivered by student mentors. In summary, this combined intervention of screening and mentor-led academic support successfully identified gaps within first-year students’ mathematical knowledge, with the workshops addressing the diversity in mathematical understanding and helping reduce a prior qualification awarding gap.

Responsible Computing: A Longitudinal Study of a Peer-led Ethics Learning Framework

We studied the impact of introducing first-year computer science (CS) students to ethical thinking about the social justice impacts of data collection, tracking, bias, internet privacy, and competitive “real world” system design and critique activities. While basic content was consistent for all, one group was involved throughout the course in peer discussions designed to foster greater engagement, with the anticipation that this would enable students to reach new levels of sensitivity through peer-to-peer interaction. This article reports on our observation of this design, interview, and project data collected throughout the course as well interviews conducted eight months later to learn about how students were retaining and applying what they learned. We found that students are sensitive to the technology-related risks and vulnerabilities encountered by individuals based on race, gender, and, to some extent, age, but they struggle to assess who is responsible for these risks, what to do about bias in technology design, and how to mitigate harms for individuals whom they perceive to be vulnerable, furthering the argument for an integrated ethics curriculum. We explore the value of formal peer-led discussion to evolve social justice thinking with a focus on identity, though note that opportunities for any group discussion are meaningful to students’ thinking about social justice. Over the longer term, students tend to recall and apply ethics that is closely related to their identity, suggesting that empathy has limits.

Impact of a Scratch programming intervention on student engagement in a Nigerian polytechnic first-year class: verdict from the observers

An engaging first programming class (CS1) often inspires students’ passion for computer science (CS). However, the evidence in the literature suggests that the average CS1 classes are anything but engaging for many students. The performance of CS compared to other science, technology, engineering, and mathematics (STEM) courses in international student engagement surveys seems to substantiate CS1 failure, attrition rates, and lack of diversity in most CS classes. Meanwhile, for its simplicity in introducing programming to beginners, primary and secondary schools use Scratch, an educational programming environment developed by the Massachusetts Institute of Technology, USA. For the same reason, higher institutions now include some forms of Scratch instruction in CS1. The question remains, to what extent is Scratch engaging, especially for students in higher education? This study addressed this gap by observing college computer science students exposed to a constructionist Scratch programming pedagogy. We adopted a descriptive design based on quantitative observations. To observe the class during a weekly 2-hour session, we employed five CS educators, one observer per week. Each observer, employing a 20-item observation protocol, rated the extent of affective, behavioral, and cognitive engagement of first-year CS students in a polytechnic in North Central Nigeria. Most of the students were learning to program for the first time. Analysis of the data showed a significant agreement in the ratings of the five observers for overall student engagement, although the impact was moderate. However, while agreement in their ratings for affective engagement was significant, with a large effect, there was no significant concordance in their ratings for behavioral engagement. Observers also significantly agreed in their ratings for cognitive engagement; however, the impact was moderate. These findings suggest that employing Scratch in higher education can be engaging and useful, especially for students with no prior programming experience.

Adopting Computer Science Pedagogical Patterns in Developing and Enhancing Computational Thinking Among Zero-Year Engineering Students

Prospective undergraduate engineering students are assumed to possess Computational Thinking (CT) skills through basic programming. They are thus required to have considerable programming experience to enable them to execute tasks involving CT skills. Unfortunately, this is not the case as existing studies demonstrate lack of these skills, leading to poor academic performance. This was the case with first-year computer science international students at Eötvös Loránd University, faculty of Informatics, prompting the management to seek the causes of poor academic performance among the students and proper interventions. Two intervention courses were created to help develop and enhance CT among the students – Introduction to Computational Thinking and Scratch Programming. &nbsp;Apart from inculcating CT among learners, another underlying concern is that teachers are not keen to embrace CT approaches in the classroom. This is attributed to lack of time in utilising machine technology and lack of pedagogical skills. This was the case when the Scratch programming course was first offered. Like teaching any other subject, CT teachers require a collection of pedagogical experiences to enable effective CT teaching, including pedagogical patterns. Therefore, this paper demonstrates the adoption of computer science (CS) pedagogical patterns in the designing and teaching Scratch Programming course to zero-year students in the faculty to develop and enhance CT. The patterns established through the literature review were deployed in the development and delivery of the course. The survey was conducted to establish the effectiveness of the tool.

Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system.

A Guided Scratch Visual Execution Environment to Introduce Programming Concepts to CS1 Students

First-year computer science (CS1) university students traditionally have difficulties understanding how to program. This paper describes research introducing CS1 students to programming concepts using a Scratch programming language guided visual execution environment (VEE). The concepts addressed are those from an introductory programming course (sequences, variables, operators, conditionals, loops, and events and parallelism). The VEE guides novice students through programming concepts, explaining and guiding interactive exercises executed in Scratch by using metaphors and serious games. The objective of this study is, firstly, to investigate if a cohort of 124 CS1 students, from three distinct groups, studying at the same university, are able to improve their programming skills guided by the VEE. Secondly, is the improvement different for various programming concepts? All the CS1 students were taught the module by the same tutor in four 2-h sessions (8 h), and a qualitative research approach was adopted. The results show students significantly improved their programming knowledge, and this improvement is significant for all the programming concepts, although greater for certain concepts such as operators, conditionals, and loops than others. It also shows that students lacked initial knowledge of events and parallelism, though most had used Scratch during their high school years. The sequence concept was the most popular concept known to them. A collateral finding in this study is how the students’ previous knowledge and learning gaps affected grades they required to access and begin study at the university level.

Teaching highly mixed-ability CS1 classes: A proposed approach.

With the increased reliance on technology, computer programming has emerged as an essential skill that is interesting to many audiences beyond merely computer scientists. As a result, many students from various disciplines take first-year computer science courses. This led to classrooms with a lot of diversity in student motivation, backgrounds, learning needs, and educational levels. Teaching the same material to such a diverse group is challenging. The aim of this paper is two-fold. Firstly, we present a flipped-based approach that benefits from the mixed-ability nature of first-year programming courses rather than considering it as a burden. Secondly, we present a study that evaluates the extent to which the proposed approach enhances student learning in such a mixed-ability environment. The study was conducted in a first-year course at the University of British Columbia – Okanagan, and it was based on three components:1) a survey of 25 Likert items(n = 46), 2) class average grade and pass rate over 6 years (n = 42 + 38 + 56 + 79 + 90 + 74), and 3) student ratings of the course over 5 years (n = 42 + 38 + 56 + 79 + 90). Findings of the survey indicate an overall positive students’ impression with no significant difference in the opinions of various student populations. Analyzing the course grades, pass rates, and student ratings confirmed the survey findings and showed an overall improvement in grades, pass-rates, and student satisfaction.

The Role of Pre-writing Strategies to Enhance the Students’ Idea Generating Abilities: The Case of First-Year Computer Science Students of Haramaya University

This research was aimed at fostering students’ idea generating abilities via the implementation of pre-writing strategies. The study adopted an action research design. The first phase involved problem identification and causes of the problems through focus group discussion and classroom observation. After identifying the challenges, systematically planned actions were implemented for eight successive weeks. During the intervention phase, awareness was created among the students on the importance of using idea-generating strategies before starting to write a text. As a result, four idea-generating strategies were employed; brainstorming, clustering, free-writing and questioning. Finally, the results of the actions taken were evaluated via observation, questionnaire, and focus group discussion. The findings reveal that the students showed interest in using idea-generating strategies, and the strategies used helped them to come up with adequate ideas in order to develop a text. The strategy also helped them to think exhaustively about what to write and how to support their argument before starting writing the actual text. Out of the four strategies employed, brainstorming was found to be the most convenient strategy to generate ideas. The strategies used were found to prevent students from unnecessary pen pause and frequent deletion of what they produced. Therefore, it is possible to comprehend from this action research that using idea-generating strategies will ease the practice of developing a text.This research was aimed at fostering students’ idea generating abilities via the implementationof pre-writing strategies. The study adopted an action research design. The first phase involvedproblem identification and causes of the problems through focus group discussion and classroomobservation. After identifying the challenges, systematically planned actions were implementedfor eight successive weeks. During the intervention phase, awareness was created among thestudents on the importance of using idea-generating strategies before starting to write a text.As a result, four idea-generating strategies were employed; brainstorming, clustering, freewritingand questioning. Finally, the results of the actions taken were evaluated via observation,questionnaire, and focus group discussion. The findings reveal that the students showed interestin using idea-generating strategies, and the strategies used helped them to come up with adequateideas in order to develop a text. The strategy also helped them to think exhaustively about whatto write and how to support their argument before starting writing the actual text. Out of the fourstrategies employed, brainstorming was found to be the most convenient strategy to generateideas. The strategies used were found to prevent students from unnecessary pen pause andfrequent deletion of what they produced. Therefore, it is possible to comprehend from this actionresearch that using idea-generating strategies will ease the practice of developing a text.

Scaffolded Block-based Instructional Tool for Linear Data Structures: A Constructivist Design to Ease Data Structures’ Understanding

Data Structures courses commonly introduce topics involving high levels of abstraction and complexity, requiring significant effort from instructors and apprentices to achieve positive outcomes from the teaching-learning process. Despite the multiple studies that have occurred within the Computer Science Education (CSE) community to understand the experiences novice programmers may have when learning how to program, there is still a lack of exploration and research on understanding these experiences in scenarios different from first-year Computer Science (CS) courses. Looking further from CS introductory courses, this paper presents the results of a pilot study that evaluated the interaction of a group of CS Colombian students with DStBlocks, which is a scaffolded block-based instructional technology, designed and developed to ease linear data structures understanding. The findings and results of this pilot study are favorable, corresponding to tests centered on user experience and learning impact.

Developing and validating test items for first-year computer science courses

We report on the development, validation, and implementation of a collection of test items designed to detect misconceptions related to first-year computer science courses. To this end, we reworked the development scheme proposed by Almstrum et al. (SIGCSE Bulletin 38(4):132–145, 2006) to include students’ artifacts and to simultaneously incorporate think-aloud interviews and flash tests. We also investigated to what extent the practical efficiency of detecting certain misconceptions could be increased without significantly affecting the sensitivity of the instrument, and present positive and negative results regarding this goal. The results of a first transfer and implementation study suggest that it is indeed possible to use the test items in a large-scale practical setting – both as diagnostic instruments and as interventions.