VLDB 2026 Research / reviewers in the wild / expert
Lasang Jimba Tamang
dblp:201/5212
· DBLP profile ↗
6ranked-venue papers
2as first author
5since 2021 · last 2022
0000-0003-4626-1516ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 5 · 2 first-author · 4 since 2021Applied, interdisciplinary, general and emerging computing · 5 · 1 first-author · 4 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Automatic Question Generation for Scaffolding Self-explanations for Code Comprehension
Lasang Jimba Tamang, Rabin Banjade, Jeevan Chapagain, Vasile Rus |
AIED (1) | 1 |
| 2022 | Preliminary Experiments with Transformer based Approaches To Automatically Inferring Domain Models from Textbooks
Rabin Banjade, Priti Oli, Lasang Jimba Tamang, Vasile Rus |
EDM | 3 |
| 2022 | DeepCode: An Annotated Set of Instructional Code Examples to Foster Deep Code Comprehension and Learning
Vasile Rus, Peter Brusilovsky, Lasang Jimba Tamang, Kamil Akhuseyinoglu, Scott Fleming |
ITS | 3 |
| 2021 | Experiments with Auto-generated Socratic Dialogue for Source Code Understanding
Zeyad Alshaikh, Lasang Jimba Tamang, Vasile Rus |
CSEDU (2) | 2 |
| 2021 | A Comparative Study of Free Self-Explanations and Socratic Tutoring Explanations for Source Code ComprehensionabstractWe present in this paper the results of a randomized control trial experiment that compared the effectiveness of two instructional strategies that scaffold learners' code comprehension processes: eliciting Free Self-Explanation and a Socratic Method. Code comprehension, i.e., understanding source code, is a critical skill for both learners and professionals. Improving learners' code comprehension skills should result in improved learning which in turn should help with retention in intro-to-programming courses which are notorious for suffering from very high attrition rates due to the complexity of programming topics. To this end, the reported experiment is meant to explore the effectiveness of various strategies to elicit self-explanation as a way to improve comprehension and learning during complex code comprehension and learning activities in intro-to-programming courses. The experiment showed pre-/post-test learning gains of 30% (M = 0.30, SD = 0.47) for the Free Self-Explanation condition and learning gains of 59% (M = 0.59,SD = 0.39) for the Socratic method. Furthermore, we investigated the behavior of the two strategies as a function of students' prior knowledge which was measured using learners' pretest score. For the Free Self-Explanation condition, there was no significant difference in mean learning gains for low vs. high knowledge students. The magnitude of the difference in performance (mean difference= 0.02,95% CI: -0.34 to 0.39) was very small (eta squared = 0.006). Likewise, the Socratic method showed no significant difference in mean learning gains between low vs. high performing students. The magnitude of the performance difference (mean difference =-0.24,95% CI: -0.534 to 0.03) was large (eta squared = 0.10). These findings suggest that eliciting self-explanations can be used as an effective strategy and that guided self-explanations as in the Socratic method condition is more effective at inducing learning gains. Lasang Jimba Tamang, Zeyad Alshaikh, Nisrine Ait Khayi, Priti Oli, Vasile Rus |
SIGCSE | 1 |
| 2020 | A Socratic Tutor for Source Code Comprehension
Zeyad Alshaikh, Lasang Jimba Tamang, Vasile Rus |
AIED (2) | 2 |