Technology-Rich Tools to Support Self-Regulated Learning and Performance in Medicine

Lajoie, Susanne P.; Naismith, Laura; Poitras, Eric; Hong, Yuan-Jin; Cruz-Panesso, Ilian; Ranellucci, John; Mamane, Samuel; Wiseman, Jeffrey

doi:10.1007/978-1-4419-5546-3_16

Cited by 31 publications

(21 citation statements)

References 20 publications

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“…ALT is a broad term that includes a variety of sophisticated learning environments such Harley et al 2016a) and simulations or virtual worlds (BioWorld; Lajoie et al 2013;Jarrell et al 2016). Although the above labels for different types of learning environments are helpful for identifying specific features, they are not mutually exclusive.…”

Section: Introductionmentioning

confidence: 99%

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

Harley

Lajoie

Frasson

et al. 2016

Int J Artif Intell Educ

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A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners' experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to date that captures this topic's breadth. We took up this grand challenge by integrating related empirical studies and existing conceptual work and proposing a theoretically-guided taxonomy for the development and improvement of emotion-aware systems. In particular, multiple strategies system developers may use to help learners experience positive emotions are mapped out, including those that require different amounts and types of information about the user, as well as when this information is required. Examples from the literature are provided to illustrate how different emotionaware system approaches can be combined to take advantage of different types of data, both prior to and during the learner-system interaction. High-level system features that emotion-aware systems can tailor to learners in order to elicit positive emotions are also described and exemplified. Theoretically, the taxonomy is primarily informed by the control-value theory of achievement emotions (Pekrun 2006(Pekrun , 2011 and its assumptions about the relationship between distal and proximal antecedents and the elicitation and and Graesser (2015) and is intended to guide the design of emotion-aware systems that can foster positive emotions during learner-system interactions through the use of varied approaches, data sources, and design features.

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Section: Introductionmentioning

confidence: 99%

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

Harley

Lajoie

Frasson

et al. 2016

Int J Artif Intell Educ

Self Cite

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“…Interdisciplinary researchers have been designing and developing computer-based learning environments (CBLEs) (e.g., multimedia, hypermedia, intelligent tutoring systems, multi-agent systems) to foster and promote effective self-regulated learning in students as they learn about various topics, such as biology, physics, and ecology (Aleven et al, 2010;Azevedo & Aleven, 2013;Biswas et al, 2010;Graesser et al, 2007;Jonassen & Land, 2012;D'Mello et al, 2013;Lajoie et al, 2013;Lester et al, 2013;Woolf, 2009).…”

Section: Introductionmentioning

confidence: 99%

Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments?

Taub¹,

Azevedo²,

Bouchet³

et al. 2014

Computers in Human Behavior

112

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“…Model tracing approaches (e.g., Anderson et al, 1990) may be more effective for resolving misconceptions related to underlying knowledge, while novice-expert overlay approaches are likely to be more effective for supporting learners in self-regulating their cognitive processes while problem-solving (Lajoie et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Feedback that addresses learner misconceptions can be provided when erroneous solution paths are identified. Examples of the use of novice-expert overlay models to promote expertise in clinical reasoning include the Diagnostic Pathfinder (Danielson et al, 2007), the NUDOV system (Wahlgren, Edelbring, Fors, Hindbeck, & Stahle, 2006), and BioWorld (Lajoie et al, 2013(Lajoie et al, , 2015. In this study, we extended the prior analytics methods we used with BioWorld to analyze learner actions in the more widely-used MedU online learning environment.…”

Section: Detecting Misconceptions In Online Learningmentioning

confidence: 99%

“…The application of learning analytics to individualize instruction is referred to as learner modeling (Baker & Siemens, 2014;Ellaway, Pusic, Gallbraith, & Cameron, 2014;Ferguson, 2012;Kay, Reimann, Diebold, & Kummerfeld, 2013). Learner models allow instructional systems to capture and analyze user interactions in order to select and deliver the most suitable instructional content (Shute & Zapata-Rivera, 2012) and personalize the delivery of feedback (Feyzi Behnagh et al, 2014;Lajoie et al, 2013) to enhance learning outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Using Learning Analytics to Identify Medical Student Misconceptions in an Online Virtual Patient Environment

Poitras

Naismith

Doleck

et al. 2015

OLJ

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This study aimed to identify misconceptions in medical student knowledge by mining user interactions in the MedU online learning environment. Data from 13000 attempts at a single virtual patient case were extracted from the MedU MySQL database. A subgroup discovery method was applied to identify patterns in learner-generated annotations and responses to multiple-choice items on the diagnosis and management of acute myocardial infarction (i.e., heart attack). First, the algorithm generated rules where single terms from the learner annotations were used to predict incorrect answers to the multiple-choice items. Second, the possible combinations of terms and their relevant synonyms were used to determine whether their inclusion led to better rates of prediction. The second step was found to significantly increase prediction precision and weighted relative accuracy, uncovering four misconceptions at a rate greater than 70%. These findings serve to inform the design of an adaptive system that tailors the delivery of formative feedback to promote better learning outcomes in the domain of clinical reasoning.

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Technology-Rich Tools to Support Self-Regulated Learning and Performance in Medicine

Cited by 31 publications

References 20 publications

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments?

Using Learning Analytics to Identify Medical Student Misconceptions in an Online Virtual Patient Environment

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