Some insights into the impact of affective information when delivering feedback to students

Cabestrero, Raúl; Quirós, Pilar; Santos, Olga C.; Salmerón-Majadas, Sergio; Uria-Rivas, Raul; Boticario, Jesús G.; Arnau, David; Arevalillo‐Herráez, Miguel; Ferri, Francesc J.

doi:10.1080/0144929x.2018.1499803

Cited by 23 publications

(18 citation statements)

References 27 publications

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“…Furthermore, substantial work has been done in the area of providing feedback using data in one or more modalities (Pardo, Poquet, Martínez-Maldonado, & Dawson, 2017). For example, EEG data and responses/clickstream data have been exploited for understanding affectional reactions to feedback (eg, Cabestrero et al, 2018;Luft, Nolte, & Bhattacharya, 2013). In a slightly different context, Andrade (2017) used multimodal data (motion and gaze) to show how students' explanations of feedback loops differ while controlling an embodied simulation.…”

Section: Related Work: Utilizing Multimodal Data To Predict Learning mentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

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Students' on‐task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference‐making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a “black‐box” approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the “black‐box” approach to a “grey‐box” approach that bridges the hypothesis/literature‐driven (feature extraction) “white‐box” approach with the computation/data‐driven (feature fusion) “black‐box” approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self‐assessment case study taking advantage of new, cutting‐edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques. What is already known about this topic Capturing and measuring learners' engagement and behavior using physiological data has been explored during the last years and exhibits great potential. Effortless behavioral patterns commonly exhibited by learners, such as “cheating,” “guessing” or “gaming the system” counterfeit the learning outcome. Multimodal data can accurately predict learning engagement, performance and processes. What this paper adds Generalizes a methodology for building machine learning pipelines for multimodal educational data, using a modularized approach, namely the “grey‐box” approach. Showcases that fusion of eye‐tracking, facial expressions and arousal data provide the best prediction of effort and performance in adaptive learning settings. Highlights the importance of fusing data from different channels to obtain the most suited combinations from the different multimodal data streams, to predict and explain effort and performance in terms of pervasiveness, mobility and ubiquity. Implications for practice and/or policy Learning analytics researchers shall be able to use an innovative methodological approach, namely the “grey‐box,” to build machine learning pipelines from multimodal data, taking advantage of artificial intelligence capabilities in any educational context. Learning design professionals shall have the opportunity to fuse specific features of the multimodal data to drive the interpretation of learning outcomes in terms of physiological learner states. The constraints from th...

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Section: Related Work: Utilizing Multimodal Data To Predict Learning mentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…rough an intelligent diagnosis mechanism, they analyzed the causes for the students' wrong actions and proposed changes and suggestions for its improvement. Cabestrero et al [17] established the PE teaching methods, which can be upgraded without altering the students' cognitive models, teaching content, and test results, and the teaching supervisors are provided with reference opinions on the evaluation of the teaching performance of the instructors.…”

Section: Introductionmentioning

confidence: 99%

Application of Computer-Aided Instruction (CAI) in the Physical Education: Survey Analysis of Chinese Universities

Teng

Cai

2021

Journal of Healthcare Engineering

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With the advent of artificial intelligence, computer-aided instruction has developed into intelligent computer-aided instruction. Computer-aided instruction not only overcomes many weaknesses of traditional physical education teaching but also significantly increases the teaching effect and efficiency. This article first studies and analyzes the characteristics of computer-aided instruction systems. Then, we use statistical methods and logical analysis to randomly select two classes of undergraduate students as the experimental and control group, respectively. In the control group, the traditional teaching model was adopted, while the experimental group adopted the computer-aided teaching mode. The experimental group showed significant improvement in physical education as compared to the control group. The results of this study validated that the use of computer-assisted instruction is beneficial in the implementation of physical education programs, mostly in teaching concepts and principles of academic nature.

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“…In this particular case, for a given user, feedback seemed more relevant when the user is preparing the tasks to be done, instead of when actually doing them. To get a deeper insight into this issue, from where to improve performance, we are planning to carry out another study with some psychologists, and design an experimental setting with their support that takes into account relevant methodological variables, as we have done in the past (see [2]). Moreover, using data mining with the most continuous and lasting tracking of a single individual could lead to finding out the most appropriate value for a given person.…”

Section: Discussionmentioning

confidence: 99%

“…Affective computing focuses on detecting emotional reactions and then applying this information to help people properly manage their emotions [1]. This is especially relevant in the educational context, where emotions can influence engagement and performance [2]. Sensor technologies for educational context facilitate the real-time management of emotions.…”

Section: Introductionmentioning

confidence: 99%

Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios

Uria-Rivas

Rodriguez‐Sanchez

Santos

et al. 2019

Sensors

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Physiological sensors can be used to detect changes in the emotional state of users with affective computing. This has lately been applied in the educational domain, aimed to better support learners during the learning process. For this purpose, we have developed the AICARP (Ambient Intelligence Context-aware Affective Recommender Platform) infrastructure, which detects changes in the emotional state of the user and provides personalized multisensorial support to help manage the emotional state by taking advantage of ambient intelligence features. We have developed a third version of this infrastructure, AICARP.V3, which addresses several problems detected in the data acquisition stage of the second version, (i.e., intrusion of the pulse sensor, poor resolution and low signal to noise ratio in the galvanic skin response sensor and slow response time of the temperature sensor) and extends the capabilities to integrate new actuators. This improved incorporates a new acquisition platform (shield) called PhyAS (Physiological Acquisition Shield), which reduces the number of control units to only one, and supports both gathering physiological signals with better precision and delivering multisensory feedback with more flexibility, by means of new actuators that can be added/discarded on top of just that single shield. The improvements in the quality of the acquired signals allow better recognition of the emotional states. Thereof, AICARP.V3 gives a more accurate personalized emotional support to the user, based on a rule-based approach that triggers multisensorial feedback, if necessary. This represents progress in solving an open problem: develop systems that perform as effectively as a human expert in a complex task such as the recognition of emotional states.

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Some insights into the impact of affective information when delivering feedback to students

Cited by 23 publications

References 27 publications

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Application of Computer-Aided Instruction (CAI) in the Physical Education: Survey Analysis of Chinese Universities

Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios

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