Haptic feedback and students’ learning about levers: Unraveling the effect of simulated touch

Wiebe, Eric N.; Minogue, James; Jones, M. Gail; Cowley, Jennifer A.; Krebs, Denise

doi:10.1016/j.compedu.2009.04.004

Cited by 51 publications

(34 citation statements)

References 26 publications

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“…However, a study by Wiebe et al (2009) revealed that students in a visual and haptic group spent a significantly longer time interacting with virtual haptic levers. It would be noteworthy to study relationships between time on task and students' interactive behaviours in different educational contexts that use visuohaptic technologies for learning.…”

Section: 1mentioning

confidence: 99%

“…For example, found only small differences in cognitive gain after students' interaction with a haptic model of a cell. Moreover, recent work by Wiebe et al (2009) showed that learners' interaction with a visual-haptic simulation of levers did not lead to an increased understanding of lever principles. An extrapolation of our results to education contexts at large is that the learning benefit of haptics is most likely a function of the pedagogical context and nature of the intended learning task.…”

Section: 4mentioning

confidence: 99%

“…Reiner (1999) has considered students' construction of force concepts during interaction with a virtual environment, while Dede, Salzman, Loftin, and Ash (2000) used a haptic virtual model to improve students understanding of kinematics. More recently, Wiebe, Minogue, Jones, Cowley, and Krebs (2009) investigated students' understanding of lever principles using a visuohaptic simulation.…”

Section: Students' Interaction and Learning With Haptic Virtual Modelmentioning

confidence: 99%

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Exploring relationships between students’ interaction and learning with a haptic virtual biomolecular model

Schönborn

Bivall

Tibell

2011

Computers & Education

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Twenty students assigned to a haptics (experimental) or no-haptics (control) condition performed a "docking" task where users sought the most favourable position between a ligand and protein molecule, while students' interactions with the model were logged. Improvement in students' understanding of biomolecular binding was previously measured by comparing written responses to a target conceptual question before and after interaction with the model. A log-profiling tool visualized students' movement of the ligand molecule during the docking task. Multivariate parallel coordinate analyses explored any relationships in the entire student data set. The haptics group produced a tighter constellation of collected final docked ligand positions in comparison with no-haptics students, coupled to docking profiles that depicted a more fine-tuned ligand traversal. Students in the no-haptics condition employed double the amount of interactive behaviours concerned with switching between different visual chemical representations offered by the model. In the no-haptics group, this visually intense processing was synonymous with erroneously 'fitting' the ligand closer distances to the protein surface. Students who showed higher learning gains tended to engage fewer visual representational switches, and were from the haptics group, while students with a higher spatial ability also engaged fewer visual representational switches, irrespective of assigned condition. From an information-processing standpoint, visual and haptic coordination may offload the visual pathway by placing less strain on visual working memory. From an embodied cognition perspective, visual and tactile sensorimotor interactions in the macroworld may provide access to constructing knowledge about submicroscopic phenomena. The results have cognitive and practical implications for the use of multimodal virtual reality technologies in educational contexts.

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Section: 1mentioning

confidence: 99%

Section: 4mentioning

confidence: 99%

Section: Students' Interaction and Learning With Haptic Virtual Modelmentioning

confidence: 99%

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Exploring relationships between students’ interaction and learning with a haptic virtual biomolecular model

Schönborn

Bivall

Tibell

2011

Computers & Education

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“…To maintain some level of ecological validity, we only tested secondary school students who were able to come to our lab, which unfortunately resulted in a low number of participants. Even though small sample sizes are common in studies with process measures (e.g., Goldberg and Wichansky 2002;Jarodzka et al 2010;Tsai et al 2012;Wiebe et al 2009), further research is urgently needed to replicate these findings with a larger sample size and in a real-life setting.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Self-regulation of secondary school students: self-assessments are inaccurate and insufficiently used for learning-task selection

et al. 2018

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Self-assessment and task selection are important self-regulated learning skills for secondary school students. More specifically, selecting new tasks based on selfassessments is very important for them, because teachers are not always present or able to select tasks for them individually. However, little is known about the processes underlying these self-regulated learning skills, and thus no guidelines exist for teaching self-assessment and the selection of subsequent learning-tasks. We propose a model for self-regulated learning-task selection (SRLTS) which represents a possible pathway for the task-selection process, and which students could use as a norm when making task selections. The model could help students to decide what possible new tasks might be suitable for their current skill level, based on self-assessments. The aim of this study is to evaluate to what extent secondary school students select learning tasks according to this model, and whether they use self-assessments to this end. Secondary school students (N = 15) selected learning tasks in the domain of genetics from a structured task database. The tasks varied in difficulty and amount of support provided (i.e., completion problems vs. traditional problems). We used eye tracking, performance estimates, estimates of mental effort, judgments of learning, and open questions to gain more insight in what students focus on and think about when selecting a task. Results suggest that students roughly follow the SRLTS model, but they base their decisions on inaccurate self-assessments. This implies that students might benefit from self-assessment and task-selection advice, which could provide feedback on self-assessments and stimulate students to use self-assessment information as input for task selection in the way the model prescribes to optimize their learning.

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“…simulating surface contact geometry, smoothness, slippage and temperature) by employing physical receptors in the hand and arm" (page 53) [26] . When force feedback simulation is combined with visual simulation students are able to have what Reiner [27] (Cited by Weiber) [26] terms as an embodied experience which resulting in the learning developing more accurate mental models and representations said to enhance learning. Hamza-Lup and Stanescu [25] argue however that it is difficult to define relevant training tools that would benefit from haptic feedback.…”

Section: Touch and Tactile Feedback In Simulated Learningmentioning

confidence: 99%

Maximising sensory learning through immersive education

Roberts

2014

JNEP

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The use of simulated learning in nurse education is not new and much has been written regarding various approaches to using low, medium and high-fidelity approaches. Reality or fidelity is important in terms of creating quality learning using simulation; however, within the literature there is a strong focus on the use of computerised mannequins, rather than on the environment in which the simulation occurs. It is accepted that scenarios on which simulation is based should represent the reality of the clinical world, where students are enabled to learn through active participation in situation which they will likely encounter in the real world. Nurses retrieve information from patients using all of their senses; indeed nursing text books advocate the use of a multi-sensory approach to assessment. Using the senses is often highlighted as part of active learning reinforcing the need for seeing, noticing and observing as a central principle; however other senses may be just as important in terms of active learning. Educators need to determine which aspects of clinical simulation are most important for learning. For example, are motor, cognitive and sensory aspects of equal importance? This paper describes the emerging technology enabling educators to introduce a range of sensory learning stimuli, for example, the use of smell as a clinical indicator and sophisticated suits which provide the wearer with tactile feedback. We go on to consider the potential value of such mechanisms to learning through simulation.

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Haptic feedback and students’ learning about levers: Unraveling the effect of simulated touch

Cited by 51 publications

References 26 publications

Exploring relationships between students’ interaction and learning with a haptic virtual biomolecular model

Exploring relationships between students’ interaction and learning with a haptic virtual biomolecular model

Self-regulation of secondary school students: self-assessments are inaccurate and insufficiently used for learning-task selection

Maximising sensory learning through immersive education

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