Design and Pilot Testing of Subgoal Labeled Worked Examples for Five Core Concepts in CS1

Subgoal learning has improved student problem-solving performance in programming, but it has been tested for only one-to-two hours of instruction at a time. Our work pioneers implementing subgoal learning throughout an entire introductory programming course. In this paper we discuss the protocol that we used to identify subgoals for core programming procedures, present the subgoal labels created for the course, and outline the subgoal-labeled instructional materials that were designed for a Java-based course. To examine the effect of subgoal labeled materials on student performance in the course, we compared quiz and exam grades between students who learned using subgoal labels and those who learned using conventional materials. Initial results indicate that learning with subgoals improves performance on early applications of concepts. Moreover, variance in performance was lower and persistence in the course was higher for students who learned with subgoals compared to those who learned with conventional materials, suggesting that learning with subgoal labels may uniquely benefit students who would normally receive low grades or dropout of the course

Using the SOLO Taxonomy to Understand Subgoal Labels Effect in CS1

is work extends previous research on subgoal labeled instructions by examining their effect across a semester-long, Java-based CS1 course. Across four quizzes, students were asked to explain in plain English the process that they would use to solve a programming problem. In this mixed methods study, we used the SOLO taxonomy to categorize student responses about problem-solving processes and compare students who learned with subgoal labels to those who did not. e use of the SOLO taxonomy classification allows us to look deeper than the mere correctness of answers to focus on the quality of the answers produced in terms of completeness of relevant concepts and explanation of relationships among concepts. Students who learned with subgoals produced higher-rated answers in terms of complexity and quality on three of four quizzes. Also, they were three times more likely to discuss issues of data type on a question about assignments and expressions than students who did not learn with subgoal labeling. is suggests that the use of subgoal labeling enabled students to gain a deeper and more complex understanding of the material presented in the course

Effect of Implementing Subgoals in Code.org\u27s Intro to Programming Unit in Computer Science Principles

The subgoal learning framework has improved performance for novice programmers in higher education, but it has only started to be applied and studied in K-12 (primary/secondary). Programming education in K-12 is growing, and many international initiatives are attempting to increase participation, including curricular initiatives like Computer Science Principles and non-profit organizations like Code.org. Given that subgoal learning is designed to help students with no prior knowledge, we designed and implemented subgoals in the introduction to programming unit in Code.org\u27s Computer Science Principles course. The redesigned unit includes subgoal-oriented instruction and subgoal-themed pre-written comments that students could add to their programming activities. To evaluate efficacy, we compared behaviors and performance of students who received the redesigned subgoal unit to those receiving the original unit. We found that students who learned with subgoals performed better on problem-solving questions but not knowledge-based questions and wrote more in open-ended response questions, including a practice Performance Task for the AP exam. Moreover, at least one-third of subgoal students continued to use the subgoal comments after the subgoal-oriented instruction had been faded, suggesting that they found them useful. Survey data from the teachers suggested that students who struggled with the concepts found the subgoals most useful. Implications for future designs are discussed

The curious case of loops

Background and Context Subgoal labeled worked examples have been extensively researched, but the research has been reported piecemeal. This paper aggregates data from three studies, including data previously unreported, to holistically examine the effect of subgoal labeled worked examples across three student populations and across different instructional designs. Objective By aggregating the data, we provide more statistical power for somewhat surprising yet replicable results. We discuss which results generalize across populations, focusing on a stable effect size for subgoal labels in programming instruction. Method We use descriptive and inferential statistics to examine the data collected from different student populations and different classroom instructional designs. We concentrate on the effect size across samples of the intervention for generalization. Findings Students using two variations of subgoal labeled instructional materials perform better than the others: the group that was given the subgoal labels with farther transfer between worked examples and practice problems and the group that constructed their own subgoal labels with nearer transfer between worked examples and practice problems

Effect of Implementing Subgoals in Code.org’s Intro to Programming unit in Computer Science Principles

The subgoal learning framework has improved performance for novice programmers in higher education, but it has only started to be applied and studied in K-12 (primary/secondary). Programming education in K-12 is growing, and many international initiatives are attempting to increase participation, including curricular initiatives like Computer Science Principles and non-profit organizations like Code.org. Given that subgoal learning is designed to help students with no prior knowledge, we designed and implemented subgoals in the introduction to programming unit in Code.org’s Computer Science Principles course. The redesigned unit includes subgoal-oriented instruction and subgoal-themed pre-written comments that students could add to their programming activities. To evaluate efficacy, we compared behaviors and performance of students who received the redesigned subgoal unit to those receiving the original unit. We found that students who learned with subgoals performed better on problem-solving questions but not knowledge-based questions and wrote more in open-ended response questions, including a practice Performance Task for the AP exam. Moreover, at least a third of subgoal students continued to use the subgoal comments after the subgoal-oriented instruction had been faded, suggesting that they found them useful. Survey data from the teachers suggested that students who struggled with the concepts found the subgoals most useful. Implications for future designs are discussed

The Curious Case of Loops

Background and Context: Subgoal labeled worked examples are effective for teaching computing concepts, but the research to date has been reported in a piecemeal fashion. This paper aggregates data from three studies, including data that has not been previously reported upon, to examine more holistically the effect of subgoal labeled worked examples across three student populations and across different instructional designs. Objective: By aggregating the data, we provide more statistical and explanatory power for somewhat surprising yet replicable results. We discuss which results generalize across populations, focusing on a stable effect size to be expected when using subgoal labels in programming instruction. Method: We use descriptive and inferential statistics to examine the data for the effect of subgoal labeled worked examples across different student populations and different classroom instructional designs. We specifically concentrate on the potential effect size across samples of the intervention for potential generalization. Findings: Two groups of students learning how to write loops using subgoal labeled instructional materials perform better than the others. The better performing groups were the group that was given the subgoal labels with farther transfer between worked examples and practice problems and the group that constructed their own subgoal labels with nearer transfer between worked examples and practice problems, both with medium-large effect sizes. Implications: For educators wishing to improve student learning using subgoal labeled materials should either provide students with subgoal labels while having them practice with a wide range of practice problems or allow students to generate their own subgoal labels and practice problems within similar contexts

Κατηγοριοποίηση και περιληπτικές αποδόσεις εργασιών συνεδρίων της ACM SIGCSE

Η παρούσα εργασία αφορά στη μελέτη εργασιών οι οποίες παρουσιάστηκαν στο συνέδριο ACM SIGCSE τις χρονιές 2016, 2017 και 2018. Αρχικά, γίνεται μια κατηγοριοποίηση, με βάση τον κύριο τομέα της Εκπαίδευσης της Πληροφορικής τον οποίο αφορά η κάθε εργασία που παρουσιάστηκε στα προαναφερθέντα συνέδρια. Οι κατηγορίες στις οποίες κατατάχθηκαν τα άρθρα είναι οι εξής: • Αξιολόγηση σπουδαστών • Ασφάλεια και προστασία της ιδιωτικής ζωής • Διαδραστικά περιβάλλοντα μάθησης • Διαφορετικότητα των φύλων/ Πολυπολιτισμικότητα • Εκπαίδευση της Μηχανικής Λογισμικού • Εισαγωγή στην Πληροφορική • Εκπαίδευση της Πληροφορικής • Ενσωμάτωση Πληροφορίας • Ηλεκτρονική μάθηση • Οπτικοποίηση • Πρότυπα αναλυτικά προγράμματα • Πρωτοβάθμια και Δευτεροβάθμια Εκπαίδευση • Συνεργατική Μάθηση • Συστήματα διαχείρισης μάθησης • Υπολογιστική Σκέψη • Υπολογιστικός Αλφαβητισμός Στη συνέχεια, δίνονται περιληπτικές αποδόσεις των εργασιών της χρονιάς 2017 που εμπίπτουν στις παρακάτω επιλεγμένες κατηγορίες: • Αξιολόγηση φοιτητών/μαθητών • Εισαγωγή στην Πληροφορική • Εκπαίδευση της Πληροφορικής • Πρωτοβάθμια και Δευτεροβάθμια Εκπαίδευση • Συνεργατική Μάθηση • Υπολογιστική ΣκέψηThis thesis focuses on the study of papers presented at the ACM SIGCSE conference in the years 2016, 2017 and 2018. Initially, a categorization is defined, based on the main areas of IT education that are included in the aforementioned conferences. The categories in which the articles were classified are: • Student evaluation • Security and Privacy • Interactive learning environments • Gender Diversity / Multiculturalism • Software engineering education • CS1 • Computer Science Education • Integration of Information • E-learning • Visualization • Model curricula • K-12 • Collaborative learning • Computational Thinking • Computing Literacy Afterwards, reviews of the papers of the year 2017 are presented concerning the following categories: • Student evaluation • CS1 • Computer Science Education • K-12 • Collaborative learning • Computational Thinkin