A Feedback System Supporting Students Approaching a High-Level Programming Course

Abstract

This study analyzes the mistakes students are prone to make in programming and uses the GDB and Valgrind tools to implement dynamic analysis techniques for their eventual application to programs created by students. In the analysis process, spectral error localization technology is used to strengthen the dynamic analysis to find errors more accurately. The analyzed results are sorted and corresponding feedback is given to students in order for them to better understand the content of errors when revising the program and classifying and counting the types of errors made. This study sorts mistakes frequently made by students and topics in which students are likely to make certain mistakes. The developed system was implemented in experiments including students from a programming course who were divided into two groups, namely the experimental group and the control group. A system for both groups of students to upload and submit assignments and a code analysis and feedback improvement system were used. Students in the control group only used the assignment uploading and submitting system for basic assignment uploading, verification, and the comparison of test data. After the program was entered, declarative sentence disassembly and dynamic slicing were suggested. Data were sent to GNU Debugger (GDB) and Valgrind for spectral error location; the classification and recording of error types; and the interpretation of the number of error lines, error types, and related variables. Feedback and a generated report were sent back to the student interface to provide effective and useful feedback to the students in the experimental group for them to revise their homework and record the types and number of errors they made in that week’s homework in the database. The answers provided by the students to the questions were recorded. The analysis of the pass rates of the students in the experimental and control groups for each homework test aided the understanding of the differences in the learning success of the two groups of students each week. The weekly pass rates and the numbers of measured errors in the experimental group compared with in the control group were input into a distribution map to allow us to better understand whether there was any positive correlation between the detected information, feedback to the students, pass rates of the tests, and other related data. The system statistically obtained feedback and the degree of improvement of homework programs; then, it distributed specially designed questionnaires to all students to directly obtain and quantify their feedback and perceived benefits of this system, thereby verifying the effectiveness of the system and its practicality.

Implementation of Assessment and Feedback in Higher Education

“…In this study, feedback is conceptualized in terms of assessment. The narrative analysis will emphasize the significance of assessment feedback in education [12,[16][17]. This research contributes to the current literature on assessment feedback by underscoring the crucial role it plays in promoting teaching and learning in the field of education.…”

“…Finally, it is important that students are adequately prepared for criticism. Data show that students have difficulty interpreting feedback or interpret it differently than instructors [15,17,26]. The effectiveness of formative assessment depends on how students receive and uses feedback.…”

Correlational Study of Students’ Moodle Log for Assessments and Feedback on Assessments on Moodle for a Mandatory Pre-Degree English Course

“…Most importantly, the network needs to be stable, and instructors and students are able to use Moodle skillfully (Peiping, 2016). At times, feedback can be misinterpreted by students or students may face problems in interpreting it (Clack & Dommett, 2021;Coelho, et al, 2022;Kuo, Lin, Wang, & Nie, 2022;Elsayed & Cakir, 2023). An online environment also can facilitate automatic feedback.…”