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THE IMPACT OF COMBINING FOLLOW-UP QUESTIONS AND WORKED EXAMPLES IN PROGRAM VISUALIZATION TOOL ON IMPROVING STUDENTS’ HELD MENTAL MODELS OF POINTERS’ VALUE AND ADDRESS ASSIGNMENT. Education & Pedagogy Journal. 2022;2(4):53-64. DOI:

Previous studies have shown that the lack of a useful mental model of pointers is one of the reasons why many novice programmers fail the data structures course. This study had two main objectives: to analyze the status of mental models of pointers (focusing on value and address assignment); and to evaluate the impact of combining worked-examples and follow-up questions in CeliotM program visualization (PV) tool in the learning of pointers. The subjects of the study were sixty-two second-year undergraduate students taking a course on data structures (PMT 221) at the College of Natural and Mathematical Sciences (CNMS) of the University of Dodoma. Data were collected using pretest and posttest questionnaires. The collected data were analyzed using descriptive statistics. The results showed that 56.5% of the students had incorrect mental models of pointers. The results also showed that using the proposed strategy improved the students’ mental models of pointers from 56.5% to 87.1%. These results contribute to our understanding of the most common misconceptions that novice students may have when learning pointers. The findings of this study confirm previous studies that when the new innovative teaching strategies are used in combination with PV tools in teaching and learning programming can help improve students’ programming comprehension.

Keywords: programming, program visualization, threshold concept, pointers, mental model, follow-up questions

The Effectiveness of Using a Congruent Visualization Framework on Learning a Data Structures Course. Education & Pedagogy Journal. 2023;2(6):60-76. DOI:

The majority of computer science (CS) educationists agree that learning the Data Structures course (CS2) is very difficult among novices due to its complexity. Consequently, learning the Data Structures course has been associated with a high failure rate. To enable learners understand data structures, algorithms visualizations (AVs) were proposed. Despite the long-term use of AVs in teaching and learning data structures, research shows that such tools have not been as pedagogically effective as expected. This study aimed to evaluate the effectiveness of using a congruent visualization (CV) framework on learning data structures. The framework employs a combination of two congruent program visualization tools, which involve machine-driven and learner-driven approaches. The effectiveness of using the CV framework was evaluated using a combination of experiment, grade analysis, and questionnaire methods. The subjects of the study were 887 first-year undergraduate students from the College of Informatics and Virtual Education (CIVE) of the University of Dodoma in Tanzania, studying the CS 122 Data Structures course. Results show that the use of the CV framework improved both students’ test performance and examination pass rates compared to the traditional approach. Students’ responses from a follow-up survey showed that the use of the CV framework increased students’ motivation and confidence in learning the Data Structures course.

Keywords: Data Structures course, Visualization, Program visualization, Algorithm Visualization, Congruent visualization framework