Name
Abstract | ||
|---|---|---|
There has been growing interest in assessing computational thinking (CT) across diverse learners beyond the traditional forms of tests and assignments. Learning games offer the potential for innovative, stealth assessments of students? implicit learning from gameplay behaviors. This paper reports on the measurement of implicit CT practices demonstrated by upper elementary- and middle-school students as they play the CT learning game Zoombinis. The process of using the gameplay log data to build valid automated detectors of students? implicit CT practices is discussed. Findings from this study provide implications for analyzing gameplay behaviors at scale, leading to the development of models for the assessment of implicit STEM learning. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.chb.2021.106707 | COMPUTERS IN HUMAN BEHAVIOR |
Keywords | DocType | Volume |
Implicit learning, Computational thinking, Learning games, Classification algorithms | Journal | 120 |
ISSN | Citations | PageRank |
0747-5632 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elizabeth Rowe | 1 | 0 | 0.34 |
| Ma. Victoria Almeda | 2 | 0 | 0.34 |
| Jodi Asbell-Clarke | 3 | 0 | 0.34 |
| Richard Scruggs | 4 | 1 | 1.36 |
| Ryan S. J. d. Baker | 5 | 1220 | 111.60 |
| Erin Bardar | 6 | 1 | 1.36 |
| Santiago Gasca | 7 | 2 | 1.77 |