Forming Teams for Teaching Programming based on Static Code Analysis

The use of team for teaching programming can be effective in the classroom because it helps students to generate and acquire new knowledge in less time, but these groups to be formed without taking into account some respects, may cause an adverse effect on the teaching-learning process. This paper proposes a tool for the formation of team based on the semantics of source code (SOFORG). This semantics is based on metrics extracted from the preferences, styles and good programming practices. All this is achieved through a static analysis of code that each student develops. In this way, you will have a record of students with the information extracted; it evaluates the best formation of teams in a given course. The team's formations are based on programming styles, skills, pair programming or with leader.Comment: 9 pages, 5 equations, 5 figures; IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012. ISSN (Online): 1694-081

A Systematic Review of Developing Team Competencies in Information Systems Education

The ability to work effectively in teams has been a key competence for information systems engineers for a long time. Gradually, more attention is being paid to developing this generic competence as part of academic curricula, resulting in two questions: how to best promote team competencies and how to implement team projects successfully. These questions are closely interwoven and need to be looked at together. To address these questions, this paper identifies relevant studies and approaches, best practices, and key findings in the field of information systems education and related fields such as computer science and business, and examines them together to develop a systematic framework. The framework is intended to categorize existing research on teams and team competencies in information systems education and to guide information systems educators in supporting teamwork and promoting team competencies in students at the course and curricular level in the context of teaching in tertiary education

Identification and Evaluation of Predictors for Learning Success and of Models for Teaching Computer Programming in Contemporary Contexts

Introductory undergraduate computer programming courses are renowned for higher than average failure and withdrawal rates when compared to other subject areas. The closer partnership between higher education and the rapidly expanding digital technology industry, as demonstrated by the establishment of new Degree Apprenticeships in computer science and digital technologies, requires efficient and effective means for teaching programming skills. This research, therefore, aimed to identify reliable predictors of success in learning programming or vulnerability to failure. The research also aimed to evaluate teaching methods and remedial interventions towards recommending a teaching model that supported and engaged learners in contemporary contexts that were relevant to the workplace. Investigation of qualifications designed to prepare students for undergraduate computer science courses revealed that A-level entrants achieved significantly higher programming grades than BTEC students. However, there was little difference between the grades of those with and those without previous qualifications in computing or ICT subjects. Analysis of engagement metrics revealed a strong correlation between extent of co-operation and programming grade, in contrast to a weak correlation between programming grade and code understanding. Further analysis of video recordings, interviews and observational records distinguished between the type of communication that helped peers comprehend tasks and concepts, and other forms of communication that were only concerned with completing tasks. Following the introduction of periodic assessment, essentially converting a single final assessment to three staged summative assessment points, it was found that failing students often pass only one of the three assignment parts. Furthermore, only 10% of those who failed overall had attempted all three assignments. Reasons for failure were attributed to ‘surface’ motivations (such as regulating efforts to achieve a minimum pass of 40%), ineffective working habits or stressful personal circumstances rather than any fundamental difficulty encountered with subject material. A key contribution to pedagogical practice made by this research is to propose an ‘incremental’ teaching model. This model is informed by educational theory and empirical evidence and comprises short cycles of three activities: presenting new topic information, tasking students with a relevant exercise and then demonstrating and discussing the exercise solution. The effectiveness of this model is evidenced by increased engagement, increased quiz scores at the end of each teaching session and increased retention of code knowledge at the end of the course

Enhancing students’ motivation to learn software engineering programming techniques: a collaborative and social interaction approach

To motivate students to study advanced programming techniques, including the use of architectural styles such as the model–view–controller pattern, we have con-ducted action research upon a project based-learning approach. In addition to collabo-ration, the approach includes students’ searching and analysis of scientific documents and their involvement in communities of practice outside academia. In this paper, we report the findings of second action research cycle, which took place throughout the fourth semester of a six-semester program. As with the previous cycle during the pre-vious academic year, students did not satisfactorily achieve expected learning out-comes. More groups completed the assigned activities, but results continue to reflect poor engagement in the communities of practice and very low performance in other learning tasks. From the collected data we have identified new approaches and recom-mendations for subsequent research.Fundação para a Ciência e Tecnologia (FCT), Portugal, for Ph.D. Grants SFRH/BD/91309/2012 and SFRH/BD/87815/201

Supporting Collaboration in Introductory Programming Classes Taught in Hybrid Mode: A Participatory Design Study

Hybrid learning modalities, where learners can attend a course in-person or remotely, have gained particular significance in post-pandemic educational settings. In introductory programming courses, novices' learning behaviour in the collaborative context of classrooms differs in hybrid mode from that of a traditional setting. Reflections from conducting an introductory programming course in hybrid mode led us to recognise the need for re-designing programming tools to support students' collaborative learning practices. We conducted a participatory design study with nine students, directly engaging them in design to understand their interaction needs in hybrid pedagogical setups to enable effective collaboration during learning. Our findings first highlighted the difficulties that learners face in hybrid modes. The results then revealed learners' preferences for design functionalities to enable collective notions, communication, autonomy, and regulation. Based on our findings, we discuss design principles and implications to inform the future design of collaborative programming environments for hybrid modes

The role of social networks in students’ learning experiences

The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in students’ learning experiences. The construction of students’ social networks, the evolution of these networks, and their effects on the students’ learning experience in a university environment are examined

Inteligencia artificial y aprendizaje colaborativo asistido por computadora en la programación: un estudio de mapeo sistemático

Objective: The Computer-Supported Collaborative Learning (CSCL) approach integrates artificial intelligence (AI) to enhance the learning process through collaboration and information and communication technologies (ICTs). In this sense, innovative and effective strategies could be designed for learning computer programming. This paper presents a systematic mapping study from 2009 to 2021, which shows how the integration of CSCL and AI supports the learning process in programming courses. Methodology: This study was conducted by reviewing data from different bibliographic sources such as Scopus, Web of Science (WoS), ScienceDirect, and repositories of the GitHub platform. It employs a quantitative methodological approach, where the results are represented through technological maps that show the following aspects: i) the programming languages used for CSCL and AI software development; ii) CSCL software technology and the evolution of AI; and iii) the ACM classifications, research topics, artificial intelligence techniques, and CSCL strategies. Results: The results of this research help to understand the benefits and challenges of using the CSCL and AI approach for learning computer programming, identifying some strategies and tools to improve the process in programming courses (e.g., the implementation of the CSCL approach strategies used to form groups, others to evaluate, and others to provide feedback); as well as to control the process and measure student results, using virtual judges for automatic code evaluation, profile identification, code analysis, teacher simulation, active learning activities, and interactive environments, among others. However, for each process, there are still open research questions. Conclusions: This work discusses the integration of CSCL and AI to enhance learning in programming courses and how it supports students' education process. No model integrates the CSCL approach with AI techniques, which allows implementing learning activities and, at the same time, observing and analyzing the evolution of the system and how its users (students) improve their learning skills with regard to programming. In addition, the different tools found in this paper could be explored by professors and institutions, or new technologies could be developed from them.Objetivo: El enfoque de aprendizaje colaborativo asistido por computadora (CSCL) integra la inteligencia artificial (IA) para mejorar el proceso de aprendizaje a través de la colaboración y las tecnologías de la información y la comunicación (TICs). En este sentido, se podrían diseñar estrategias innovadoras y efectivas para el aprendizaje de la programación de computadoras. Este artículo presenta un estudio sistemático de mapeo de los años 2009 a 2021, el cual muestra cómo la integración del CSCL y la IA apoya el proceso de aprendizaje en cursos de programación. Metodología: Este estudio se realizó mediante una revisión de datos proveniente de distintas fuentes bibliográficas como Scopus, Web of Science (WoS), ScienceDirect y repositorios de la plataforma GitHub. El trabajo emplea un enfoque metodológico cuantitativo, en el cual los resultados se representan a través de mapas tecnológicos que muestran los siguientes aspectos: i) los lenguajes de programación utilizados para el desarrollo de software de CSCL e IA; ii) la tecnología de software CSCL y la evolución de la IA; y iii) las clasificaciones, los temas de investigación, las técnicas de inteligencia artificial y las estrategias de CSCL de la ACM. Resultados: Los resultados de esta investigación ayudan a entender los beneficios y retos de usar el enfoque de CSCL e IA para el aprendizaje de la programación de computadoras, identificando algunas estrategias y herramientas para mejorar el proceso en cursos de programación (e.g., La implementación de estrategias del enfoque CSCL utilizadas para formar grupos, de otras para evaluar y de otras para brindar retroalimentación); así como para monitorear el proceso y medir los resultados de los estudiantes utilizando jueces virtuales para la evaluación automática del código, identificación de perfiles, análisis de código, simulación de profesores, actividades de aprendizaje activo y entornos interactivos, entre otros. Sin embargo, aún hay preguntas investigación por resolver para cada proceso. Conclusiones: Este trabajo discute la integración del CSCL y la IA para mejorar el aprendizaje en cursos de programación y cómo esta apoya el proceso educativo de los estudiantes. Ningún modelo integra el enfoque CSCL con técnicas de IA, lo cual permite implementar actividades de aprendizaje y, al mismo tiempo, observar y analizar la evolución del sistema y de la manera en que sus usuarios (estudiantes) mejoran sus habilidades de aprendizaje con respecto a la programación. Adicionalmente, las diferentes herramientas encontradas en este artículo podrían ser exploradas por profesores e instituciones, o podrían desarrollarse nuevas tecnologías a partir de ellas

Exploring student perceptions about the use of visual programming environments, their relation to student learning styles and their impact on student motivation in undergraduate introductory programming modules

My research aims to explore how students perceive the usability and enjoyment of visual/block-based programming environments (VPEs), to what extent their learning styles relate to these perceptions and finally to what extent these tools facilitate student understanding of basic programming constructs and impact their motivation to learn programming

Learning Approach, Thinking Style and Critical Inquiry: The Online Community

The study examined if a thematically designed online introductory psychology course set in a cooperative and collaborative learning environment led to deeper learning. Using the revised two-factor Study Process Questionnaire (R-SPQ-2F; Biggs, Kember & Leung, 2001), the study predicted peer and teacher guided asynchronous dialogue would lead to increasing students’ self-perceptions of deeper learning approaches (DA) and higher levels of thinking. Individual thinking style (ITS; Sternberg & Wagner, 1992) was presumed to be an important mediator on both student pre- and post-DA scores. It was also hypothesized that thinking styles would influence student perceptions towards participating in a learning community, as measured by the Classroom Community Scale (CCS; Rovai, 2002). Contrary to the hypotheses, thinking styles didn’t predict either pre- or post DA nor end of semester CCS scores. The two main hypotheses, premised on Vygotsky’s theory of social constructivism and post Vygotskian thinking on conceptual learning, demonstrated mixed results. The expected increase in self perceptions of deep learning and a predictive relationship between DA and CCS to reflect this contextualized learning were not found. While post DA scores weren’t significantly correlated with CCS, CCS was correlated with students’ perceptions of which types of discussions guided their learning. Qualitative evidence from the online dialogue demonstrated deeper, conceptual and applied understanding than students’ self-reports. What requires further study is whether students develop an explicit metacognitive understanding of how cooperative discussions aren’t an added burden, but rather, a means of constructing a deeper meaning and approach to learning