Finding Predictors of Success in Novice Programmers\u27 Editing and Testing Behaviors

Growth in demand for employees with programming proficiency necessitates a workforce that is correctly and efficiently trained in programming fundamentals. Previous research has found correlations between intermediate programmers’ program-development habits and their success in computer science courses, but to date, these approaches have not worked well when predicting the success of students in their first course. This research project is an examination of the Normalized Programming State Model’s applicability to novice programmers, after modifying it to potentially improve its ability to detect flaws in the programs these students write. We compared the adapted model’s predictive power with that of the previous implementation of the model for novice programming data collected by BlueJ

Study of the impact of social learning and gamification methodologies on learning results in higher education

In this work, as the last step of a longitudinal study of the impact of so- cial learning and gamification methodologies on learning results in higher education, we have recorded the activity in a software platform based on Moodle, especially built for encouraging online participation of the stu- dents to design, carry out and evaluate a set of learning tasks and games, during two consecutive editions of an undergraduate course. Our aim is to confirm the relationships of the patterns of accomplishment of the gam- ified activities and the network structure of the social graphs associated to the online forums with knowledge adquisition and final outcomes. For this purpose we have offered two learning paths, traditional and novel, to our students. We have identified course variables that quantitatively explain the improvements reported when using the innovative methodolo- gies integrated in the course design, and we have applied techniques from the social network analysis (SNA) and the machine learning/deep learn- ing (ML/DL) domains to conduct success/failure classification methods finding that, generally, very good results are obtained when an ensemble approach is used, that is, when we blend the predictions made by different classifiers. The proposed methodology can be used over reduced datasets and variable time windows for having early estimates that allow pedagog- ical interventions. Finally, we have applied other statistical tests to our datasets, that confirm the influence of learning path on learning results

Toward Predicting Success and Failure in CS2: A Mixed-Method Analysis

Factors driving success and failure in CS1 are the subject of much study but less so for CS2. This paper investigates the transition from CS1 to CS2 in search of leading indicators of success in CS2. Both CS1 and CS2 at the University of North Carolina Wilmington (UNCW) are taught in Python with annual enrollments of 300 and 150 respectively. In this paper, we report on the following research questions: 1) Are CS1 grades indicators of CS2 grades? 2) Does a quantitative relationship exist between CS2 course grade and a modified version of the SCS1 concept inventory? 3) What are the most challenging aspects of CS2, and how well does CS1 prepare students for CS2 from the student's perspective? We provide a quantitative analysis of 2300 CS1 and CS2 course grades from 2013--2019. In Spring 2019, we administered a modified version of the SCS1 concept inventory to 44 students in the first week of CS2. Further, 69 students completed an exit questionnaire at the conclusion of CS2 to gain qualitative student feedback on their challenges in CS2 and on how well CS1 prepared them for CS2. We find that 56% of students' grades were lower in CS2 than CS1, 18% improved their grades, and 26% earned the same grade. Of the changes, 62% were within one grade point. We find a statistically significant correlation between the modified SCS1 score and CS2 grade points. Students identify linked lists and class/object concepts among the most challenging. Student feedback on CS2 challenges and the adequacy of their CS1 preparations identify possible avenues for improving the CS1-CS2 transition.Comment: The definitive Version of Record was published in 2020 ACM Southeast Conference (ACMSE 2020), April 2-4, 2020, Tampa, FL, USA. 8 page

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Analyzing Social Construction of Knowledge Online by Employing Interaction Analysis, Learning Analytics, and Social Network Analysis

This article examines research methods for analyzing social construction of knowledge in online discussion forums. We begin with an examination of the Interaction Analysis Model (Gunawardena, Lowe, & Anderson, 1997) and its applicability to analyzing social construction of knowledge. Next, employing a dataset from an online discussion, we demonstrate how interaction analysis can be supplemented by employing other research techniques such as learning analytics and Social Network Analysis that shed light on the social dynamics that support knowledge construction. Learning analytics is the application of quantitative techniques for analyzing large volumes of distributed data ( big data ) in order to discover the factors that contribute to learning (Long & Siemens, 2011, p. 34). Social Network Analysis characterizes the information infrastructure that supports the construction of knowledge in social contexts (Scott, 2012). By combining interaction analysis with learning analytics and Social Network Analysis, we were able to conceptualize the process by which knowledge construction takes place in online platforms

Self-beliefs in the introductory programming lab and game-based fantasy role-play

This thesis was submitted for the degree of Doctor of philosophy and awarded by Brunel University LondonIt is important for students to engage in adequate deliberate practice in order to develop programming expertise. However, students often encounter anxiety when they begin to learn. This can present a challenge to educators because such anxiety can influence practice behaviour. This thesis situates this challenge within the Control- Value Theory of Achievement Emotions, emphasising a need for domain-specific research and presenting new research tools which can be used to investigate the area. Analysis of data collected from three cohorts of introductory programming students on web programming (2011-12) and robot programming (2012-13 and 2013-14) courses show that programming self-concept and programming aptitude mindset can predict programming anxiety and that programming anxiety is negatively correlated with programming practice. However, levels of anxiety remained consistently high across this period. A method to enrich these psychological constructs through a multimedia-rich learning environment is proposed. Drawing upon the interplay between narrative reinforcement and procedural rhetoric that can be achieved in a fantasy role-play, students' self-concept can be enhanced. A double-blind randomised controlled trial demonstrates promising results, however small effect sizes suggest further research is needed

Pervasive learning analytics for fostering learners' self-regulation

Today's tertiary STEM (Science, Technology, Engineering and Mathematics) education in Europe poses problems to both teachers and students. With growing enrolment numbers, and numbers of teaching staff that are outmatched by this growth, student-teacher contact becomes more and more difficult to provide. Therefore, students are required to quickly adopt self-regulated and autonomous learning styles when entering European universities. Furthermore, teachers are required to divide their attention between large numbers of students. As a consequence, classical teaching formats of STEM education which often encompass experimentation or active exploration, become harder to implement. Educational software holds the promise of easing these problems, or, if not fully solving, at least of making them less acute: Learning Analytics generated by such software can foster self-regulation by providing students with both formative feedback and assessments. Educational software, in form of collaborative social media, makes it easier for teachers to collaborate, allows to reduce their workload and enables learning and teaching formats otherwise infeasible in large classes. The contribution of this thesis is threefold: Firstly, it reports on a social medium for tertiary STEM education called "Backstage2 / Projects" aimed specifically at these points: Improving learners' self-regulation by providing pervasive Learning Analytics, fostering teacher collaboration so as to reduce their workload, and providing means to deploy a variety of classical and novel learning and teaching formats in large classes. Secondly, it reports on several case studies conducted with that medium which point at the effectiveness of the medium and its provided Learning Analytics to increase learners' self-regulation, reduce teachers' workload, and improve how students learn. Thirdly, this thesis reports on findings from Learning Analytics which could be used in the future in designing further teaching and learning formats or case studies, yielding a rich perspective for future research and indications for improving tertiary STEM education