Detecting threshold concepts through Bayesian knowledge tracing: examining research skill development in biological sciences at the doctoral level.
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| Title: | Detecting threshold concepts through Bayesian knowledge tracing: examining research skill development in biological sciences at the doctoral level. |
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| Authors: | Kang, Jina1 (AUTHOR) jinakang@illinois.edu, Baker, Ryan2 (AUTHOR), Feng, Zhang3 (AUTHOR), Na, Chungsoo3 (AUTHOR), Granville, Peter4 (AUTHOR), Feldon, David F.3 (AUTHOR) |
| Source: | Instructional Science. Jun2022, Vol. 50 Issue 3, p475-497. 23p. |
| Subject Terms: | *Research skills, *Academic discourse, *Doctoral students, Life sciences, Research & development |
| Abstract: | Threshold concepts are transformative elements of domain knowledge that enable those who attain them to engage domain tasks in a more sophisticated way. Existing research tends to focus on the identification of threshold concepts within undergraduate curricula as challenging concepts that prevent attainment of subsequent content until mastered. Recently, threshold concepts have likewise become a research focus at the level of doctoral studies. However, such research faces several limitations. First, the generalizability of findings in past research has been limited due to the relatively small numbers of participants in available studies. Second, it is not clear which specific skills are contingent upon mastery of identified threshold concepts, making it difficult to identify appropriate times for possible intervention. Third, threshold concepts observed across disciplines may or may not mask important nuances that apply within specific disciplinary contexts. The current study therefore employs a novel Bayesian knowledge tracing (BKT) approach to identify possible threshold concepts using a large data set from the biological sciences. Using rubric-scored samples of doctoral students' sole-authored scholarly writing, we apply BKT as a strategy to identify potential threshold concepts by examining the ability of performance scores for specific research skills to predict score gains on other research skills. Findings demonstrate the effectiveness of this strategy, as well as convergence between results of the current study and more conventional, qualitative results identifying threshold concepts at the doctoral level. [ABSTRACT FROM AUTHOR] |
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| Database: | Education Research Complete |
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| Abstract: | Threshold concepts are transformative elements of domain knowledge that enable those who attain them to engage domain tasks in a more sophisticated way. Existing research tends to focus on the identification of threshold concepts within undergraduate curricula as challenging concepts that prevent attainment of subsequent content until mastered. Recently, threshold concepts have likewise become a research focus at the level of doctoral studies. However, such research faces several limitations. First, the generalizability of findings in past research has been limited due to the relatively small numbers of participants in available studies. Second, it is not clear which specific skills are contingent upon mastery of identified threshold concepts, making it difficult to identify appropriate times for possible intervention. Third, threshold concepts observed across disciplines may or may not mask important nuances that apply within specific disciplinary contexts. The current study therefore employs a novel Bayesian knowledge tracing (BKT) approach to identify possible threshold concepts using a large data set from the biological sciences. Using rubric-scored samples of doctoral students' sole-authored scholarly writing, we apply BKT as a strategy to identify potential threshold concepts by examining the ability of performance scores for specific research skills to predict score gains on other research skills. Findings demonstrate the effectiveness of this strategy, as well as convergence between results of the current study and more conventional, qualitative results identifying threshold concepts at the doctoral level. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00204277 |
| DOI: | 10.1007/s11251-022-09578-5 |
