Experiences Using Heat Maps to Help Students Find Their Bugs

Edmison, Bob; Edwards, Stephen H.

doi:10.1145/3287324.3287474

Cited by 6 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overlay graph permits to know (booleanly) if a particular skill has been developed or not, however, knowing details such as the exact programming exercise (or even more accurately the exact code snippet) where the student developed the skill can be of great value. The Heat Maps technique applied by Edmison & Edwards (2019) focuses on highlighting source code fragments. This feature inspired us to implement a complementary detailed log in which every identified evidence can point to a code snippet responsible for the skill assessment.…”

Section: Learner Modelmentioning

confidence: 99%

A-Learn EvId: A Method for Identifying Evidence of Computer Programming Skills Through Automatic Source Code Assessment

Porfírio

Pereira

Maschio

2021

RBIE

View full text Add to dashboard Cite

Contextualized in the teaching of computer programming in Computing courses, this research investigates aspects and strategies for automatic source code assessment. Continuous on-time assessment of source codes produced by students is a challenging task for teachers. The literature presents different methods for automatic assessment of source code, mostly focusing on technical aspects, such as functional correctness assessment and error detection. This paper presents the A-Learn EvId method, having as the main characteristic its focus on the assessment of high-level skills instead of technical aspects. Automatically assessing high-level skills gives insights into the thought process students used to elaborate their responses, contributing to quality and timely feedback generation. The method is characterized by three fundamental steps: (1) inserting students' source code as input data; (2) identifying evidence of skills through automatic strategies; and (3) representing identified skills through a student model. The following contributions are highlighted: updating the state of the art on the topic; a set of 37 skills identifiable through 9 automatic source code assessment strategies; construction of datasets totaling 8651 source codes.

show abstract

Section: Learner Modelmentioning

confidence: 99%