Understanding Test Takers' Choices in a Self-Adapted Test: A Hidden Markov Modeling of Process Data

Arieli‐Attali, Meirav; Ou, Lu; Simmering, Vanessa R.

doi:10.3389/fpsyg.2019.00083

Cited by 13 publications

(7 citation statements)

References 48 publications

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“…Last but not least, the statistical model simultaneously represents both between-and within-subject qualitative and quantitative variability that is prevalent in students' learning processes. It extends many traditional models, including finite mixture models [8,9], hidden Markov models [5,20], and regime-switching dynamic models [7,14], to enable crucial inferences of students' learning processes to inform interventions.…”

Section: Introductionmentioning

confidence: 99%

Using a cluster-based regime-switching dynamic model to understand embodied mathematical learning

Ou¹,

Andrade²,

Alberto

et al. 2020

Proceedings of the Tenth International Conference on Learning Analytics &Amp; Knowledge

Self Cite

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Embodied learning and the design of embodied learning platforms have gained popularity in recent years due to the increasing availability of sensing technologies. In our study, we made use of the Mathematical Imagery Trainer for Proportion (MIT-P) that uses a touchscreen tablet to help students explore the concept of mathematical proportion. The use of sensing technologies provides an unprecedented amount of high-frequency data on students' behaviors. We investigated a statistical model called mixture Regime-Switching Hidden Logistic Transition Process (mixRHLP) and fit it to the students' hand motion data. Simultaneously, the model finds characteristic regimes and assigns students to clusters of regime transitions. To understand the nature of these regimes and clusters, we explore some properties in students' and tutor's verbalization associated with these different phases.

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Section: Introductionmentioning

confidence: 99%

Using a cluster-based regime-switching dynamic model to understand embodied mathematical learning

Ou¹,

Andrade²,

Alberto

et al. 2020

Proceedings of the Tenth International Conference on Learning Analytics &Amp; Knowledge

Self Cite

View full text Add to dashboard Cite

show abstract

“…This sort of data contained in log files, referred to as process data in this paper, provides information beyond response data that typically show correctness or incorrectness only. This additional information holds promise to help us understand the strategies that underlie proficient performance and identify key actions that lead to success or failure (e.g., Arieli‐Attali et al, 2019; Han et al, 2019; He & von Davier, 2015, 2016; Liao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring latent states of problem‐solving competence using hidden Markov model on process data

Xiao

Veldkamp

et al. 2021

Computer Assisted Learning

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The response process of problem‐solving items contains rich information about respondents' behaviours and cognitive process in the digital tasks, while the information extraction is a big challenge. The aim of the study is to use a data‐driven approach to explore the latent states and state transitions underlying problem‐solving process to reflect test‐takers' behavioural patterns, and to investigate how these states and state transitions could be associated with test‐takers' performance. We employed the Hidden Markov Modelling approach to identify test takers' hidden states during the problem‐solving process and compared the frequency of states and/or state transitions between different performance groups. We conducted comparable studies in two problem‐solving items with a focus on the US sample that was collected in PIAAC 2012, and examined the correlation between those frequencies from two items. Latent states and transitions between them underlying the problem‐solving process were identified and found significantly different by performance groups. The groups with correct responses in both items were found more engaged in tasks and more often to use efficient tools to solve problems, while the group with incorrect responses was found more likely to use shorter action sequences and exhibit hesitative behaviours. Consistent behavioural patterns were identified across items. This study demonstrates the value of data‐driven based HMM approach to better understand respondents' behavioural patterns and cognitive transmissions underneath the observable action sequences in complex problem‐solving tasks.

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“…On the other hand, HMMs have extensively been used to model the behavior of individual students regarding their engagement and their motivation towards the learning procedure [2,10,18,50,54,62]. Arieli-Attali et al [1] used a HMM to learn about test takers' choice-making behavior in a self-adapted test, Shih et al [52] proposed a HMM that could discover student learning tactics, Tadayon and Pottie [59] and He and Gao [19] used a HMM to analyze and make predictions of the students' performance in educational games, and Jeong et al [25] used HMMs to examine the effect of metacognitive prompting on students' learning in the context of our computer-based learning-by-teaching environment.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Sequence Data with Markov Chain Models in Scientific Experiments

et al. 2021

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Virtual reality-based instruction is becoming an important resource to improve learning outcomes and communicate handson skills in science laboratory courses. Our study attempts first to investigate whether a Markov chain model can predict the students' performance in conducting an experiment and whether simulations improve learner achievement in handling lab equipment and conducting science experiments in physical labs. In the present study, three cohorts of graduate students are trained on a microscopy experiment using different teaching methodologies. The effectiveness of the teaching strategies is evaluated by observing the sequences of students' actions, while engaging in the microscopy experiment in real-lab situations. The students' ability in performing the science experiment is estimated by sequential analysis using a Markov chain model. According to the Markov chain analysis, the students who are trained via a virtual reality software exhibit a higher probability to perform the steps of the experiment without difficulty and without assistance than their fellow students who attend more traditional training scenarios. Our study indicates that a Markov chain model is a powerful tool that can lead to a dynamic evaluation of the students' performance in science experiments by tracing the students' knowledge states and by predicting their innate abilities.

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Understanding Test Takers' Choices in a Self-Adapted Test: A Hidden Markov Modeling of Process Data

Cited by 13 publications

References 48 publications

Using a cluster-based regime-switching dynamic model to understand embodied mathematical learning

Using a cluster-based regime-switching dynamic model to understand embodied mathematical learning

Exploring latent states of problem‐solving competence using hidden Markov model on process data

Analyzing Sequence Data with Markov Chain Models in Scientific Experiments

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