Understanding Student Attention to Adaptive Hints with Eye-Tracking

Muir, Mary; Conati, Cristina

doi:10.1007/978-3-642-28509-7_15

Cited by 8 publications

(7 citation statements)

References 12 publications

(9 reference statements)

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“…This comes from the focus of a workshop series on Personalization Approaches in Learning Environments (PALE) held annually in conjunction with the conference on User Modeling, Adaptation and Personalization (UMAP) over the last five years, which considered the different and complementary perspectives in which personalization can be addressed in learning environments. The scope includes: i) intelligent tutoring systems (Janning et al 2016;Arevalillo-Herráez et al 2014;Arevalillo-Herráez et al 2013;Costa et al 2012), ii) educational recommender systems (Greer et al 2015;Henning et al 2014;Labaj and Bieliková 2013;Manjarrés-Riesco et al 2013;Nussbaumer et al 2012;Roldan et al 2011;Berthold et al 2011;Thai-Nghe et al 2011;Minguillon et al 2011), iii) learning management systems (Tang and Yacef 2015;Chacón-Rivas et al 2015), iv) personal learning environments Berthold et al 2011), v) educational games (Ghergulescu and Muntean 2016;Pentel 2015;Leite et al 2011;Frias-Martinez and Virseda 2011;Muir et al 2011), vi) agent-based learning environments (Dennis et al 2016;Tamayo and Perez-Marin 2012;Ginon et al 2012;Redondo-Hernandez and Perez-Marin 2011), vii) multi-user virtual environments (Ocumpaugh et al 2014), and viii) other ad-hoc approaches (Sawadogo et al 2014;Koch et al 2013). …”

Section: Introductionmentioning

confidence: 98%

“…More specifically, as far as interactions and technological devices are concerned, there are publications on detecting learners' interactions from diverse sources, such as i) observations (Ocumpaugh et al 2014), ii) input devices such as mice (Pentel 2015;Labaj and Bieliková 2013), iii) videocameras (Koch et al 2013;Leite et al 2011), iv) touch gestures (Koch et al 2013), v) social interactions (Lobo et al 2014;Ming and Ming 2012), vi) eye-tracking (Labaj and Bieliková 2013;Muir et al 2011), and vii) physiological sensors (Ghergulescu and Muntean 2016). They also consider diverse technological devices including mobiles (Frias-Martinez and Virseda 2011), tablets (Koch et al 2013) and tabletops (Roldan et al 2011).…”

Section: Introductionmentioning

confidence: 98%

“…Specific domains that have been addressed include: i) STEM (i.e., Science, Technology, Engineering and Math) education (Greer et al 2015;Silva et al 2015;Arevalillo-Herráez et al 2014;Arevalillo-Herráez et al 2013;Tamayo and PerezMarin 2012;Costa et al 2012;Muir et al 2011), ii) higher education (Henning et al 2014;Labaj and Bieliková 2013;Ming and Ming 2012), iii) distance learning scenarios , iv) vocational learning (Tang and Yacef 2015), v) after-school support (Leite et al 2011;Frias-Martinez and Virseda 2011), and vi) MOOCs (i.e., Massive Open Online Courses) (Henning et al 2014;Tang and Kay 2014).…”

Section: Introductionmentioning

confidence: 99%

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Preface to Special Issue on User Modelling to Support Personalization in Enhanced Educational Settings

Santos

Kravčík

Boticario

2016

Int J Artif Intell Educ

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Personalization approaches in learning environments aim to foster effective, active, efficient, and satisfactory learning. Suitable user modelling techniques are crucial to support these approaches in dealing with learners' needs within realistic learning environments, which are currently cropping up in a varied range of situations. Bearing this in mind, this paper provides an overview of relevant research over the last five years in both user modelling and education, which shows an increasing interest among researchers and practitioners who are concerned with modelling users' needs in the new and evolving educational settings that are widening the diversity of learning contexts and issues to be considered. In particular, we have identified three main areas of research: i) modelling of learners and their performance to provide engaging learning experiences, ii) designing adaptive support, and iii) building standards-based models to cope with interoperability and portability.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Preface to Special Issue on User Modelling to Support Personalization in Enhanced Educational Settings

Santos

Kravčík

Boticario

2016

Int J Artif Intell Educ

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“…Those results were based on hint display time (duration of time a hint stays open on the screen) as a rough indication of attention. In [13], however, initial results based on the analysis of gaze data from two Prime Climb players suggested that students sometimes pay attention to hints. The results we present here confirm this finding and extend it by presenting an analysis of factors that impact attention.…”

Section: Related Workmentioning

confidence: 99%

“…The third contribution of our work is that we use eye-tracking data to study user attention patterns to the adaptive-hints, an approach not previously investigated in hint-related research. In [13], we presented a preliminary qualitative analysis of eye-tracking data for two students playing Prime Climb, and edu-game for number factorization. In this paper, we extend that work by presenting a more extensive quantitative analysis based on data from 12 students.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Attention to Student – Adaptive Hints in an Educational Game

Muir

Conati

2012

Intelligent Tutoring Systems

Self Cite

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Abstract. We present a user study to investigate which factors affect student attention to user-adaptive hints during interaction with an educational computer game. The game is Prime Climb, an educational game designed to provide individualized support for learning number factorization skills in the form of hints based on a model of student learning. We use eye-tracking data to capture user attention patterns on the game adaptive-hints, and present results on how these patterns are impacted by factors related to existing user knowledge, hint timing, and attitude toward getting help in general. We plan to leverage these results in the future for making hint delivery adaptive to these factors. Keywords: Adaptive help, educational games, pedagogical agents, eye-tracking IntroductionThe ability of providing interventions that are adaptive to each student's specific needs is one of the distinguishing features of intelligent tutoring systems (ITS). One of the most widespread forms of adaptive interventions is to provide hints designed to gradually help students through specific educational activities when they have difficulties proceeding on their own [14]. Despite the wide adoption of adaptive hints, there is an increasing body of research showing their possible limitations, going from students gaming the system, i.e., using the hints to get quick answers from the ITS [see 7 for an overview], to help avoidance, i.e. students not using hints altogether [e.g., 8, 9]. In this paper, we are interested in investigating the latter issue. More specifically, we seek to gain a better understanding of which factors may affect a student's tendency to attend to adaptive hints that the student has not explicitly elicited. This research has three main contributions to the ITS field. First, while previous work on help avoidance focused on capturing and responding to a student's tendency to avoid requesting hints [e.g., 8, 9], here we investigate how students react when the hints are provided unsolicited. A second contribution is that we look at attention to adaptive hints during interaction with an educational computer game (edu-game), whereas most previous work on student usage (or misusage) of hints has been in the context of more structured problem solving activities. Providing adaptive hints to support learning during game play is especially challenging because it requires a trade-off between fostering learning and maintaining engagement. We see the results

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A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

Liu,

Wang,

et al. 2023

J. Comput. Sci. Technol.

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Understanding Student Attention to Adaptive Hints with Eye-Tracking

Cited by 8 publications

References 12 publications

Preface to Special Issue on User Modelling to Support Personalization in Enhanced Educational Settings

Preface to Special Issue on User Modelling to Support Personalization in Enhanced Educational Settings

An Analysis of Attention to Student – Adaptive Hints in an Educational Game

A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

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