Dataset Information

Teaching Assistant Attention and Responsiveness to Student Reasoning in Written Work.

ABSTRACT: Instructors communicate what they value about students' written work through their comments and feedback, and this feedback has the potential to direct how students approach writing assignments. In this study, we examined how graduate student teaching assistants (TAs) attended and responded to students' written lab reports in an introductory biology course. We collected and analyzed marked lab reports from five TAs and interviewed them about their marking decisions. The results show that TAs attended mainly to writing style and form in their markings and comments on lab reports. However, there were occasions when they attended to students' scientific reasoning in their markings and during interviews. We provide evidence that TAs' understanding of the purpose of the laboratory course and assessment structure influenced their attention. We also provide evidence that TAs could shift their attention from style and form to reasoning in response to moment-to-moment contextual cues. Building on these results, we discuss course design and professional development that reframes labs and reports to focus on students' biological reasoning.

SUBMITTER: Hill CFC

PROVIDER: S-EPMC5998315 | biostudies-other | 2018 Jun

REPOSITORIES: biostudies-other

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Teaching Assistant Attention and Responsiveness to Student Reasoning in Written Work.

Hill Cynthia F C CFC Gouvea Julia S JS Hammer David D

CBE life sciences education 20180601 2

Instructors communicate what they value about students' written work through their comments and feedback, and this feedback has the potential to direct how students approach writing assignments. In this study, we examined how graduate student teaching assistants (TAs) attended and responded to students' written lab reports in an introductory biology course. We collected and analyzed marked lab reports from five TAs and interviewed them about their marking decisions. The results show that TAs att ...[more]

PMID: 29749844

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Project description:PurposeDeveloping medical students' clinical reasoning requires a structured longitudinal curriculum with frequent targeted assessment and feedback. Performance-based assessments, which have the strongest validity evidence, are currently not feasible for this purpose because they are time-intensive to score. This study explored the potential of using machine learning technologies to score one such assessment-the diagnostic justification essay.MethodFrom May to September 2018, machine scoring algorithms were trained to score a sample of 700 diagnostic justification essays written by 414 third-year medical students from the Southern Illinois University School of Medicine classes of 2012-2017. The algorithms applied semantically based natural language processing metrics (e.g., coherence, readability) to assess essay quality on 4 criteria (differential diagnosis, recognition and use of findings, workup, and thought process); the scores for these criteria were summed to create overall scores. Three sources of validity evidence (response process, internal structure, and association with other variables) were examined.ResultsMachine scores correlated more strongly with faculty ratings than faculty ratings did with each other (machine: .28-.53, faculty: .13-.33) and were less case-specific. Machine scores and faculty ratings were similarly correlated with medical knowledge, clinical cognition, and prior diagnostic justification. Machine scores were more strongly associated with clinical communication than were faculty ratings (.43 vs .31).ConclusionsMachine learning technologies may be useful for assessing medical students' long-form written clinical reasoning. Semantically based machine scoring may capture the communicative aspects of clinical reasoning better than faculty ratings, offering the potential for automated assessment that generalizes to the workplace. These results underscore the potential of machine scoring to capture an aspect of clinical reasoning performance that is difficult to assess with traditional analytic scoring methods. Additional research should investigate machine scoring generalizability and examine its acceptability to trainees and educators.

Project description:Background:Medical students transitioning into professional practice feel underprepared to deal with the emotional complexities of real-life ethical situations. Simulation-based learning (SBL) may provide a safe environment for students to probe the boundaries of ethical encounters. Published studies of ethics simulation have not generated sufficiently deep accounts of student experience to inform pedagogy. The aim of this study was to understand students' lived experiences as they engaged with the emotional challenges of managing clinical ethical dilemmas within a SBL environment. Methods:This qualitative study was underpinned by an interpretivist epistemology. Eight senior medical students participated in an interprofessional ward-based SBL activity incorporating a series of ethically challenging encounters. Each student wore digital video glasses to capture point-of-view (PoV) film footage. Students were interviewed immediately after the simulation and the PoV footage played back to them. Interviews were transcribed verbatim. An interpretative phenomenological approach, using an established template analysis approach, was used to iteratively analyse the data. Results:Four main themes emerged from the analysis: (1) 'Authentic on all levels?', (2)'Letting the emotions flow', (3) 'Ethical alarm bells' and (4) 'Voices of children and ghosts'. Students recognised many explicit ethical dilemmas during the SBL activity but had difficulty navigating more subtle ethical and professional boundaries. In emotionally complex situations, instances of moral compromise were observed (such as telling an untruth). Some participants felt unable to raise concerns or challenge unethical behaviour within the scenarios due to prior negative undergraduate experiences. Conclusions:This study provided deep insights into medical students' immersive and embodied experiences of ethical reasoning during an authentic SBL activity. By layering on the human dimensions of ethical decision-making, students can understand their personal responses to emotion, complexity and interprofessional working. This could assist them in framing and observing appropriate ethical and professional boundaries and help smooth the transition into clinical practice.

Dataset Information

Teaching Assistant Attention and Responsiveness to Student Reasoning in Written Work.

Publications

Teaching Assistant Attention and Responsiveness to Student Reasoning in Written Work.

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