Collaboration Expertise in Medicine - No Evidence for Cross-Domain Application from a Memory Retrieval Study

Background Is there evidence for expertise on collaboration and, if so, is there evidence for cross-domain application? Recall of stimuli was used to measure so-called internal collaboration scripts of novices and experts in two studies. Internal collaboration scripts refer to an individual's knowledge about how to interact with others in a social situation. Method-Study 1 Ten collaboration experts and ten novices of the content domain social science were presented with four pictures of people involved in collaborative activities. The recall texts were coded, distinguishing between superficial and collaboration script information. Results-Study 1 Experts recalled significantly more collaboration script information (M = 25.20;SD = 5.88) than did novices (M = 13.80;SD = 4.47). Differences in superficial information were not found. Study 2 Study 2 tested whether the differences found in Study 1 could be replicated. Furthermore, the cross-domain application of internal collaboration scripts was explored. Method-Study 2 Twenty collaboration experts and 20 novices of the content domain medicine were presented with four pictures and four videos of their content domain and a video and picture of another content domain. All stimuli showed collaborative activities typical for the respective content domains. Results-Study 2 As in Study 1, experts recalled significantly more collaboration script information of their content domain (M = 71.65;SD = 33.23) than did novices (M = 54.25;SD = 15.01). For the novices, no differences were found for the superficial information nor for the retrieval of collaboration script information recalled after the other content domain stimuli. Discussion There is evidence for expertise on collaboration in memory tasks. The results show that experts hold substantially more collaboration script information than did novices. Furthermore, the differences between collaboration novices and collaboration experts occurred only in their own content domain, indicating that internal collaboration scripts are not easily stored and retrieved in memory tasks other than in the own content domain

Challenges in the Automatic Analysis of Students' Diagnostic Reasoning

Diagnostic reasoning is a key component of many professions. To improve students' diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning self-explanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task's characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges

High-fidelity simulation increases obstetric self-assurance and skills in undergraduate medical students

Objective: Teaching intrapartum care is one of the most challenging tasks in undergraduate medical education. High-fidelity obstetric simulators might support students' learning experience. The specific educational impact of these simulators compared with traditional methods of model-based obstetric teaching has not yet been determined. Study design: We randomly assigned 46 undergraduate medical students to be taught using either a high-fidelity simulator or a scale wood-and-leather phantom. Their self-assessments were evaluated using a validated questionnaire. We assessed obstetric skills and asked students to solve obstetric paper cases. Main outcome measures: Assessment of fidelity-specific teaching impact on procedural knowledge, motivation, and interest in obstetrics as well as obstetric skills using high- and low-fidelity training models. Results: High-fidelity simulation specifically improved students' feeling that they understood both the physiology of parturition and the obstetric procedures. Students in the simulation group also felt better prepared for obstetric house jobs and performed better in obstetric skills evaluations. However, the two groups made equivalent obstetric decisions. Conclusion: This study provides first data on the impact of high-fidelity simulation in an undergraduate setting

Improving knowledge and changing behavior towards guideline based decisions in diabetes care: a controlled intervention study of a team-based learning approach for continuous professional development of physicians.

Continuing Professional Development (CPD) courses should ideally improve a physician's knowledge and change their professional behavior in daily practice towards a best clinical practice reference model and guideline adherence. Interactive methods such as team-based learning and case-based learning, as compared to lectures, can impart sustainable knowledge and lead to high satisfaction among participants. We designed an interactive case-based CPD-seminar on diabetes care using a team-based learning approach to evaluate whether it leads to an improvement of short-term knowledge and changing of behavior towards guideline based decisions and how this learning approach is perceived by participants

Learning to diagnose collaboratively – Effects of adaptive collaboration scripts in agent-based medical simulations

We investigated how medical students' collaborative diagnostic reasoning, particularly evidence elicitation and sharing, can be facilitated effectively using agent-based simulations. Providing adaptive collaboration scripts has been suggested to increase effectiveness, but existing evidence is diverse and could be affected by unsystematic group constellations. Collaboration scripts have been criticized for undermining learners' agency. We investigate the effect of adaptive and static scripts on collaborative diagnostic reasoning and basic psychological needs. We randomly allocated 160 medical students to one of three groups: adaptive, static, or no collaboration script. We found that learning with adaptive collaboration scripts enhanced evidence sharing performance and transfer performance. Scripting did not affect learners’ perceived autonomy and social relatedness. Yet, compared to static scripts, adaptive scripts had positive effects on perceived competence. We conclude that for complex skills complementing agent-based simulations with adaptive scripts seems beneficial to help learners internalize collaboration scripts without negatively affecting basic psychological needs

Diagnostic argumentation in teacher education: Making the case for justification, disconfirmation, and transparency

Research on diagnosing in teacher education has primarily emphasized the accuracy of diagnostic judgments and has explained it in terms of factors such as diagnostic knowledge. However, approaches to scientific argumentation and information processing suggest differentiating between diagnostic judgment and diagnostic argumentation: When making accurate diagnostic judgments, the underlying reasoning can remain intuitive, whereas diagnostic argumentation requires controlled and explicable reasoning about a diagnostic problem to explain the reasoning in a comprehensible and persuasive manner. We suggest three facets of argumentation for conceptualizing diagnostic argumentation, which are yet to be addressed in teacher education research: justification of a diagnosis with evidence, disconfirmation of differential diagnoses, and transparency regarding the processes of evidence generation. Therefore, we explored whether preservice teachers’ diagnostic argumentation and diagnostic judgment might represent different diagnostic skills. We also explored whether justification, disconfirmation, and transparency should be considered distinct subskills of preservice teachers’ diagnostic argumentation. We reanalyzed data of 118 preservice teachers who learned about students’ learning difficulties with simulated cases. For each student case, the preservice teachers had to indicate a diagnostic judgment and provide a diagnostic argumentation. We found that preservice teachers’ diagnostic argumentation seldom involved all three facets, suggesting a need for more specific training. Moreover, the correlational results suggested that making accurate diagnostic judgments and formulating diagnostic argumentation may represent different diagnostic skills and that justification, disconfirmation, and transparency may be considered distinct subskills of diagnostic argumentation. The introduced concepts of justification, disconfirmation, and transparency may provide a starting point for developing standards in diagnostic argumentation in teacher education

Learning to diagnose accurately through virtual patients: do reflection phases have an added benefit?

BACKGROUND Simulation-based learning with virtual patients is a highly effective method that could potentially be further enhanced by including reflection phases. The effectiveness of reflection phases for learning to diagnose has mainly been demonstrated for problem-centered instruction with text-based cases, not for simulation-based learning. To close this research gap, we conducted a study on learning history-taking using virtual patients. In this study, we examined the added benefit of including reflection phases on learning to diagnose accurately, the associations between knowledge and learning, and the diagnostic process. METHODS A sample of N = 121 medical students completed a three-group experiment with a control group and pre- and posttests. The pretest consisted of a conceptual and strategic knowledge test and virtual patients to be diagnosed. In the learning phase, two intervention groups worked with virtual patients and completed different types of reflection phases, while the control group learned with virtual patients but without reflection phases. The posttest again involved virtual patients. For all virtual patients, diagnostic accuracy was assessed as the primary outcome. Current hypotheses were tracked during reflection phases and in simulation-based learning to measure diagnostic process. RESULTS Regarding the added benefit of reflection phases, an ANCOVA controlling for pretest performance found no difference in diagnostic accuracy at posttest between the three conditions, F(2, 114) = 0.93, p = .398. Concerning knowledge and learning, both pretest conceptual knowledge and strategic knowledge were not associated with learning to diagnose accurately through reflection phases. Learners' diagnostic process improved during simulation-based learning and the reflection phases. CONCLUSIONS Reflection phases did not have an added benefit for learning to diagnose accurately in virtual patients. This finding indicates that reflection phases may not be as effective in simulation-based learning as in problem-centered instruction with text-based cases and can be explained with two contextual differences. First, information processing in simulation-based learning uses the verbal channel and the visual channel, while text-based learning only draws on the verbal channel. Second, in simulation-based learning, serial cue cases are used to gather information step-wise, whereas, in text-based learning, whole cases are used that present all data at once

Using natural language processing to support peer‐feedback in the age of artificial intelligence: a cross‐disciplinary framework and a research agenda

Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments

qˉq{\bar {q}}q condensate for light quarks beyond the chiral limit

We determine the ${\bar{q}}q$ condensate for quark masses from zero up to that of the strange quark within a phenomenologically successful modelling of continuum QCD by solving the quark Schwinger-Dyson equation. The existence of multiple solutions to this equation is the key to an accurate and reliable extraction of this condensate using the operator product expansion. We explain why alternative definitions fail to give the physical condensate.Comment: 13 pages, 8 figure

Using natural language processing to support peer‐feedback in the age of artificial intelligence: A cross‐disciplinary framework and a research agenda

Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments