Purpose and Level of Feedback in an Exploratory Learning Environment for Fractions

Holmes, W.; Mavrikis, Manolis; Hansen, Alice; Grawemeyer, Beate

doi:10.1007/978-3-319-19773-9_76

Cited by 9 publications

(11 citation statements)

References 11 publications

(10 reference statements)

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“…-OTHER PROBLEM SOLVING feedback. This aimed to help students tackle a problem by challenging their thinking instead of specifying next steps (a subset of 'Socratic' formative feedback, Holmes et al, 2015b). -REFLECTIVE prompts.…”

Section: -Instructive Feedback This Feedback Provided Detailed Instrmentioning

confidence: 99%

“…Other research works, including our own Holmes et al, 2015a) and more recently that of Basu et al (2017) in this issue, focus on how formative feedback in open-ended or exploratory environments can sca↵old learners to perform a particular learning task. However, in addition to providing context-specific guidance, our formative feedback also aims to enhance student a↵ective states -i.e., to move students from nominally negative a↵ective states (such as frustration or boredom) into nominally positive a↵ec-tive states or to maintain positive a↵ective states.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Affective learning: improving engagement and enhancing learning with affect-aware feedback

Grawemeyer

Mavrikis

Holmes

et al. 2017

User Model User-Adap Inter

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This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students' a↵ective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students' a↵ective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students' performance (the non-a↵ect condition). Our results suggest that a↵ect-aware support contributes to reducing boredom and o↵-task behavior, and may have an e↵ect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that a↵ect-aware modelling plays in the adaptive delivery of formative feedback to support learning.

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Section: -Instructive Feedback This Feedback Provided Detailed Instrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Affective learning: improving engagement and enhancing learning with affect-aware feedback

Grawemeyer

Mavrikis

Holmes

et al. 2017

User Model User-Adap Inter

Self Cite

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show abstract

“…Some key differences, however, were unanticipated. For example, while in the UK we had employed several 'levels' of feedback [10], including the intentional ambiguity of Socratic questioning, early trials made clear that more work was required to ensure that this approach was appropriate in China. On the other hand, between the two contexts, there were clear observed similarities in the attitudes of the participating students and their teachers.…”

Section: Findings and Discussionmentioning

confidence: 99%

Fractions Lab Goes East: Learning and Interaction with an Exploratory Learning Environment in China

Mavrikis

Holmes

Zhang

et al. 2018

Lecture Notes in Computer Science

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In a bid to better understand cultural differences and feed into the design of an exploratory learning environment for learning fractions in China, we conducted a study in three schools in Beijing. A mixed methods protocol was followed involving 186 children. In this paper, we report several results, including a paired t-test suggesting a significant difference between pre-and post-tests and effect sizes warranting further research. Beyond learning gains, we also report preliminary results from analysis of student interaction data that points to similarities as well as differences between UK and China. This is important because it helps us determine next steps in terms of the design, implementation and integration of the technology in the two contexts, and raises future hypothesis.

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“…The adaptive immediate feedback provided to the experimental group participants was designed to arouse meaningful reflective learning from continuous interaction between the learners and the smartphone-based training [ 46 , 47 ]. As didactic-procedural form of feedback [ 48 ] aligned to our theoretical framing, it was designed to force the health care provider to contemplate over the incorrect care provision choices they provided in their failed attempt (eg, “ Some of the selected actions are not appropriate at this stage ” focuses on the number of wrong choices and their placement within the clinical care-giving pathway and is meant to force reflection as to which stage they are most appropriate). This feedback was provided to the experiment arm after each incorrect attempt at a learning task with three cascading detail levels based on the predicted probability that the learner’s next attempt was going to be correct.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Adaptive Feedback in a Smartphone-Based Game on Health Care Providers’ Learning Gain: Randomized Controlled Trial

Tuti¹,

Winters²,

Edgcombe³

et al. 2020

J Med Internet Res

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Background Although smartphone-based emergency care training is more affordable than traditional avenues of training, it is still in its infancy, remains poorly implemented, and its current implementation modes tend to be invariant to the evolving learning needs of the intended users. In resource-limited settings, the use of such platforms coupled with gamified approaches remains largely unexplored, despite the lack of traditional training opportunities, and high mortality rates in these settings. Objective The primary aim of this randomized experiment is to determine the effectiveness of offering adaptive versus standard feedback, on the learning gains of clinicians, through the use of a smartphone-based game that assessed their management of a simulated medical emergency. A secondary aim is to examine the effects of learner characteristics and learning spacing with repeated use of the game on the secondary outcome of individualized normalized learning gain. Methods The experiment is aimed at clinicians who provide bedside neonatal care in low-income settings. Data were captured through an Android app installed on the study participants’ personal phones. The intervention, which was based on successful attempts at a learning task, included adaptive feedback provided within the app to the experimental arm, whereas the control arm received standardized feedback. The primary end point was completion of the second learning session. Of the 572 participants enrolled between February 2019 and July 2019, 247 (43.2%) reached the primary end point. The primary outcome was standardized relative change in learning gains between the study arms as measured by the Morris G effect size. The secondary outcomes were the participants individualized normalized learning gains. Results The effect of adaptive feedback on care providers’ learning gain was found to be g=0.09 (95% CI −0.31 to 0.46; P=.47). In exploratory analysis, using normalized learning gains, when subject-treatment interaction and differential time effect was controlled for, this effect increased significantly to 0.644 (95% CI 0.35 to 0.94; P<.001) with immediate repetition, which is a moderate learning effect, but reduced significantly by 0.28 after a week. The overall learning change from the app use in both arms was large and may have obscured a direct effect of feedback. Conclusions There is a considerable learning gain between the first two rounds of learning with both forms of feedback and a small added benefit of adaptive feedback after controlling for learner differences. We suggest that linking the adaptive feedback provided to care providers to how they space their repeat learning session(s) may yield higher learning gains. Future work might explore in more depth the feedback content, in particular whether or not explanatory feedback (why answers were wrong) enhances learning more than reflective feedback (information about what the right answers are). Trial Registration Pan African Clinical Trial Registry (PACTR) 201901783811130; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=5836 International Registered Report Identifier (IRRID) RR2-10.2196/13034

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Purpose and Level of Feedback in an Exploratory Learning Environment for Fractions

Cited by 9 publications

References 11 publications

Affective learning: improving engagement and enhancing learning with affect-aware feedback

Affective learning: improving engagement and enhancing learning with affect-aware feedback

Fractions Lab Goes East: Learning and Interaction with an Exploratory Learning Environment in China

Evaluation of Adaptive Feedback in a Smartphone-Based Game on Health Care Providers’ Learning Gain: Randomized Controlled Trial

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