Applying IRT to Estimate Learning Ability and K-means Clustering in Web based Learning

Chang, Wen-Chih; Yang, Haijiao

doi:10.4304/jsw.4.2.167-174

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Expert-based [88,102]; Statistic-based [29,31,108,139,170]; Deep Learning: CNN [79], RNN [75,90,122], NLP [119,172].…”

Section: Question Bank Construction Sectionmentioning

confidence: 99%

“…Despite its resourceintensive nature, involving pre-testing with examinees, it utilizes parameter estimation methods or gradient descent on CDM (e.g., IRT) to estimate question parameters [45]. In some psychological theories, like Classic Test Theory (CTT), question difficulty is calculated as the proportion of correct responses within examinees [49,108], while discrimination is derived from performance disparities between higher and lower ability examinees [29]. The Q-matrix is another crucial characteristic of questions.…”

Section: Statistic-based Characteristics Annotationmentioning

confidence: 99%

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Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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“…Expert-based [88,102]; Statistic-based [29,31,108,139,170]; Deep Learning: CNN [79], RNN [75,90,122], NLP [119,172].…”

Section: Question Bank Construction Sectionmentioning

confidence: 99%

Section: Statistic-based Characteristics Annotationmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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“…Some academics in the industrial sector choose the K-means algorithm and the hierarchical clustering algorithm to classify temperature fluctuations. Some researchers developed self-organizing feature mapping (SOM) based on the K-means method ( Wu, Zhao & Guo, 2020 ; Chang & Yang, 2009 ; Lee & Macqueen, 1980 ; Qin & Gui, 2022 ). They employed a SOM training data set and K-means clustering based on the output findings of the training set to generate superior clustering results, which led to the visualization and understanding of the model being successful ( Beauchaine & Beauchaine III, 2002 ).…”

Section: Related Workmentioning

confidence: 99%

Assessment of college students’ mental health status based on temporal perception and hybrid clustering algorithm under the impact of public health events

Li,

2023

PeerJ Computer Science

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The dynamic landscape of public health occurrences presents a formidable challenge to the emotional well-being of college students, necessitating a precise appraisal of their mental health (MH) status. A pivotal metric in this realm is the Mental Health Assessment Index, a prevalent gauge utilized to ascertain an individual’s psychological well-being. However, prevailing indices predominantly stem from a physical vantage point, neglecting the intricate psychological dimensions. In pursuit of a judicious evaluation of college students’ mental health within the crucible of public health vicissitudes, we have pioneered an innovative metric, underscored by temporal perception, in concert with a hybrid clustering algorithm. This augmentation stands poised to enrich the extant psychological assessment index framework. Our approach hinges on the transmutation of temporal perception into a quantifiable measure, harmoniously interwoven with established evaluative metrics, thereby forging a novel composite evaluation metric. This composite metric serves as the fulcrum upon which we have conceived a pioneering clustering algorithm, seamlessly fusing the fireworks algorithm with K-means clustering. The strategic integration of the fireworks algorithm addresses a noteworthy vulnerability inherent to K-means—its susceptibility to converging onto local optima. Empirical validation of our paradigm attests to its efficacy. The proposed hybrid clustering algorithm aptly captures the dynamic nuances characterizing college students’ mental health trajectories. Across diverse assessment stages, our model consistently attains an accuracy threshold surpassing 90%, thus outshining existing evaluation techniques in both precision and simplicity. In summation, this innovative amalgamation presents a formidable stride toward an augmented understanding of college students’ mental well-being during times of fluctuating public health dynamics.

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“…In addition, Lee and Cho (2013) stated many e-learning and assessment systems based on IRT are mainly concerned with the ability estimation in order to suggest adjusting learning content or change the test difficulty level in a more customized learning setup. Chang and Yang (2009) also stated that other applications firstly applied IRT for capability estimation and further used classification methods for student rank. According to Lazarsfeld (1958), item responses being statistically independent, given the respondent's location in latent space, is a further critical assumption in IRT.…”

Section: Item Response Theory (Irt)mentioning

confidence: 99%

The European Conference on Education 2022: Official Conference Proceedings

2022

ECE Official Conference Proceedings

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Education has been affected by the spread of COVID-19, and schools were closed for almost 2 years in the Czech Republic. During the pandemic, teacher stress has been intensified by distance education as well as by limited access to social support, which might function as a buffer in experiencing stress. The aim of our study was to analyze experience and attitudes of teachers to the online distant teaching during the Covid-19 pandemic and to confront them with the results of their discomfort level. The survey was realized in a group of 492 teachers who exercised their profession during the quarantine. Most teachers reported high level of stress during online teaching due to their effort to offer good performance. Teachers also stated their work involvement required serious and accurate preparation for classes which was time consuming. Results in a standardized inventory showed high level of teacher emotional exhaustion, but did not show higher level in depersonalization and personal accomplishment.

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Applying IRT to Estimate Learning Ability and K-means Clustering in Web based Learning

Cited by 7 publications

References 5 publications

Geography of Technology Transfer in China

Geography of Technology Transfer in China

Assessment of college students’ mental health status based on temporal perception and hybrid clustering algorithm under the impact of public health events

The European Conference on Education 2022: Official Conference Proceedings

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