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Abstract It is important but challenging for prospective health professionals to learn the visual distinction between potentially harmful and harmless skin lesions, such as malignant melanomas and benign nevi. Knowledge about factors related to diagnostic performance is sparse but a prerequisite for designing and evaluating evidence-based educational interventions. Hence, this study explored how the characteristics of 240 skin lesions, the number of classified lesions and the response times of 137 laypeople were related to performance in diagnosing pigmented skin cancer. Our results showed large differences between the lesions, as some were classified correctly by more than 90% and others by less than 10% of the participants. A t-test showed that for melanomas, the correct diagnosis was provided significantly more often than for nevi. Furthermore, we found a significant Pearson correlation between the number of solved tasks and performance in the first 50 diagnostic tasks. Finally, t-tests for investigating the response times revealed that compared to true decisions, participants spent longer on false-negative but not on false-positive decisions. These results provide novel knowledge about performance-related factors that can be useful when designing diagnostic tests and learning interventions for melanoma detection.

Abstract A frequent concern about constructivist instruction is that it works well, mainly for students with higher domain knowledge. We present findings from a set of two quasi-experimental pretest-intervention-posttest studies investigating the relationship between prior math achievement and learning in the context of a specific type of constructivist instruction, Productive Failure. Students from two Singapore public schools with significantly different prior math achievement profiles were asked to design solutions to complex problems prior to receiving instruction on the targeted concepts. Process results revealed that students who were significantly dissimilar in prior math achievement seemed to be strikingly similar in terms of their inventive production, that is, the variety of solutions they were able to design. Interestingly, it was inventive production that had a stronger association with learning from PF than pre-existing differences in math achievement. These findings, consistent across both topics, demonstrate the value of engaging students in opportunities for inventive production while learning math, regardless of prior math achievement.

Abstract Current trend in neurosciences is to use naturalistic stimuli, such as cinema, class-room biology or video gaming, aiming to understand the brain functions during ecologically valid conditions. Naturalistic stimuli recruit complex and overlapping cognitive, emotional and sensory brain processes. Brain oscillations form underlying mechanisms for such processes, and further, these processes can be modified by expertise. Human cortical functions are often analyzed with linear methods despite brain as a biological system is highly nonlinear. This study applies a relatively robust nonlinear method, Higuchi fractal dimension (HFD), to classify cortical functions of math experts and novices when they solve long and complex math demonstrations in an EEG laboratory. Brain imaging data, which is collected over a long time span during naturalistic stimuli, enables the application of data-driven analyses. Therefore, we also explore the neural signature of math expertise with machine learning algorithms. There is a need for novel methodologies in analyzing naturalistic data because formulation of theories of the brain functions in the real world based on reductionist and simplified study designs is both challenging and questionable. Data-driven intelligent approaches may be helpful in developing and testing new theories on complex brain functions. Our results clarify the different neural signature, analyzed by HFD, of math experts and novices during complex math and suggest machine learning as a promising data-driven approach to understand the brain processes in expertise and mathematical cognition.

Background: Despite acquiring vast content knowledge about the functioning of the human body through university teaching, medical students struggle to transfer that knowledge to one of the core disciplinary practices – differential diagnosis. The authors aimed to overcome this problem by implementing computer-based virtual environment simulations in medical education courses. Methods: In an experimental study, the authors compared problem-solving in medical computer-based virtual environment simulations prior to instruction with an instruction-first approach. They compared the effects on isomorphic testing and transfer performance of clinical knowledge and clinical reasoning skills as well as evoked learning mechanisms. The study took place in spring 2021 with undergraduate medical students in the scope of a medical trajectory course. Due to Corona-Virus-19 measures participants completed all study activities remotely from home. Results: The authors did not find any learning activity sequence to be superior to the other. However, when looking at the two learning activities individually, they found that problem-solving in computer-based virtual environment simulations and direct instruction might be equally effective for learning content knowledge. Nevertheless, problem-solving in computer-based virtual environment simulations with formative feedback might be more effective for learning clinical reasoning skills than mere instruction. Conclusions: The findings indicate that problem-solving in computer-based virtual environment simulations might be more effective for learning clinical reasoning skills than mere theoretical instruction. The present study has a high level of ecological validity because it took place in a realistic setting where students had to perform all learning and testing tasks autonomously.

The current levels of enthusiasm for flipped learning are not commensurate with and far exceed the vast variability of scientific evidence in its favor. We examined 46 meta-analyses only to find remarkably different overall effects, raising the question about possible moderators and confounds, showing the need to control for the nature of the intervention. We then conducted a meta-analysis of 173 studies specifically coding the nature of the flipped implementation. In contrast to many claims, most in-class sessions are not modified based on the flipped implementation. Furthermore, it was flipping followed by a more traditional class and not active learning that was more effective. Drawing on related research, we proposed a more specific model for flipping, “Fail, Flip, Fix, and Feed” whereby students are asked to first engage in generating solutions to novel problems even if they fail to generate the correct solutions, before receiving instructions.

Learning is a process that leads to outcomes. The science of learning is the epistemic practice of investigating the relationship between this process and its outcomes. We propose a novel method for the study of learning: The Double Treatment experimental design. The core design for experimental studies in the Learning Sciences consists of two elements, learning activities together with assessments of their outcomes. In conventional Single Treatment experimental designs, the learning activities are subject to a controlled experimental manipulation, the outcomes of which are then evaluated in a common assessment. In the Double Treatment experimental design, both the learning activity and the assessment are subject to the experimental manipulation, leading to a basic 2 × 2 experimental design. We provide a theoretical rationale in favor of the Double Treatment design, which we illustrate and discuss in three experimental study examples implementing this method: A study on the contextual nature of memorization and recall while scuba diving, a study on invention activities as a preparation for future learning, and a study on resource-rich assessment in computer science. We argue that the Double Treatment design has the potential to enhance the epistemic power of experimental Learning Sciences research by improving the ontological coherence of experimental designs. They promise to facilitate both hypothesis testing and hypothesis generation, enable the validation of assessment methods from within corresponding studies, and reduce the need for externally validated assessments outside the control of the researchers.

Background: Haptic feedback has been shown to be an effective facilitator of the learning of scientific concepts in a series of studies. However, little is known about the underlying salient learning mechanisms, which are activated when learning from haptic feedback. Objectives: We investigate the learning mechanism in a higher chemistry education setting, in which the students learned about the abstract concept of potential energy in a quantum chemical context. Methods: In this work, we present a mixed-methods problem-solving prior to instruction (PS-I) study with chemistry bachelor students. In an interactive quantum chemistry learning environment, the students explored the energetic profiles of nucleophilic substitution reactions and the corresponding activation barriers experienced as repulsion and attraction. We measured cognitive and affective mechanisms which have been shown to be activated in PS-I designs, such as knowledge gap awareness, state curiosity, and positive affect as well as the learning outcome quantitatively and the learning trajectories qualitatively. Results and Conclusions: Planned contrasts revealed a small hindering effect of the haptic feedback on the learning outcome. Thematic analysis of the qualitative data lets us attribute this effect to the lack of scaffolding of the haptic feedback and the spontaneous atomic movements in the simulation, which may be interpreted as a visual representation of force in itself. This work highlights the importance of accurate mappings of the haptic feedback unto the target domain and further delivers insight into the prior conceptions of chemistry undergraduate students.

A frequent concern about constructivist instruction is that it works well, mainly for students with higher domain knowledge. We present findings from a set of two quasi-experimental pretest-intervention-posttest studies investigating the relationship between prior math achievement and learning in the context of a specific type of constructivist instruction, Productive Failure. Students from two Singapore public schools with significantly different prior math achievement profiles were asked to design solutions to complex problems "prior" to receiving instruction on the targeted concepts. Process results revealed that students who were significantly dissimilar in prior math achievement seemed to be strikingly similar in terms of their inventive production, that is, the variety of solutions they were able to design. Interestingly, it was inventive production that had a stronger association with learning from PF than pre-existing differences in math achievement. These findings, consistent across both topics, demonstrate the value of engaging students in opportunities for inventive production while learning math, regardless of prior math achievement.