The Department of Chemistry and Biochemistry at St. Mary's College of Maryland has scaffolded collaboration skills throughout the Biochemistry curriculum and developed several assessment tools to evaluate these skills. Biochemistry I and II have used team contracts at the beginning of extensive team projects where students identify their strengths, review expectations, and plan for group communication. At the conclusion of each project, each student assesses their own contributions and team members for various parts of the project. A common collaboration rubric was also applied in Biochemistry I and II as well as in two other courses, General Chemistry II Lab and Physical Chemistry I Lab, for students to evaluate themself and team members using the following subcategories: quality of work, commitment, leadership, communication, and analysis. In Biochemistry I and II, we used this rubric for multiple assignments that are part of the projects in the lecture courses. In the General Chemistry II Lab, we provided elements of this rubric within an evaluation form that reflects these collaboration attributes after each lab experience, so students can assess and report privately on their experiences as part of their collaboration grade for the course. A similar collaboration rubric is completed by students for each team‐based laboratory within Physical Chemistry I. We also demonstrate different ways that instructors can use the data from these assessment tools. In our department, we are using these tools to frame the importance of collaboration skills and collecting data to inform our teaching of these skills. Preliminary data suggest that our curriculum is successfully teaching students how to be good collaborators.
Exploring the process of group-based collaboration: A validation argument for a collaboration model and observation rubric for training explainable machine learning models.
Collaboration is an important learning process. During collaborative learning, students engage in group activities where they converge on goals, solve problems and make joint decisions. To understand the process of collaboration, we focused on how behavior and interaction patterns contribute to the social-relational space of collaboration. We have designed a multilayered conceptual model for the collaboration process and an observation rubric that identifies behaviors and interactions during collaboration that serves as the foundation for machine learning models that can provide behavioral insight into the process of collaboration. This study reports results on several validation studies performed to establish a validation argument for our collaboration conceptual model and collaboration rubric. Through disconfirming evidence, interrater reliability testing, expert reviews, and focus group interviews, we found that our stratified architecture of collaboration and rubric provide valid accounts and descriptions of human behavior and interactions that can be used to substantiate the collaboration process.
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- Award ID(s):
- 2016849
- PAR ID:
- 10461795
- Date Published:
- Journal Name:
- Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Constructed responses can be used to assess the complexity of student thinking and can be evaluated using rubrics. The two most typical rubric types used are holistic and analytic. Holistic rubrics may be difficult to use with expert-level reasoning that has additive or overlapping language. In an attempt to unpack complexity in holistic rubrics at a large scale, we have developed a systematic approach called deconstruction. We define deconstruction as the process of converting a holistic rubric into defining individual conceptual components that can be used for analytic rubric development and application. These individual components can then be recombined into the holistic score which keeps true to the holistic rubric purpose, while maximizing the benefits and minimizing the shortcomings of each rubric type. This paper outlines the deconstruction process and presents a case study that shows defined concept definitions for a hierarchical holistic rubric developed for an undergraduate physiology-content reasoning context. These methods can be used as one way for assessment developers to unpack complex student reasoning, which may ultimately improve reliability and validation of assessments that are targeted at uncovering large-scale complex scientific reasoning.more » « less
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Constructed responses can be used to assess the complexity of student thinking and can be evaluated using rubrics. The two most typical rubric types used are holistic and analytic. Holistic rubrics may be difficult to use with expert-level reasoning that has additive or overlapping language. In an attempt to unpack complexity in holistic rubrics at a large scale, we have developed a systematic approach called deconstruction. We define deconstruction as the process of converting a holistic rubric into defining individual conceptual components that can be used for analytic rubric development and application. These individual components can then be recombined into the holistic score which keeps true to the holistic rubric purpose, while maximizing the benefits and minimizing the shortcomings of each rubric type. This paper outlines the deconstruction process and presents a case study that shows defined concept definitions for a hierarchical holistic rubric developed for an undergraduate physiology-content reasoning context. These methods can be used as one way for assessment developers to unpack complex student reasoning, which may ultimately improve reliability and validation of assessments that are targeted at uncovering large-scale complex scientific reasoning.more » « less
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