Unbounded Human Learning

Reddy, Siddharth; Labutov, Igor; Banerjee, Siddhartha; Joachims, Thorsten

doi:10.1145/2939672.2939850

Cited by 37 publications

(4 citation statements)

References 12 publications

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“…Second, when used in this second, relative sense, it offers great 3 Note that the Ď x j value is also termed the 0PL in some settings. However, there is ambiguity in how this term is used with some usage indicating predictions invariant across persons (as used here; see the '0PL-item' model in Reddy, Labutov, Banerjee, & Joachims, 2016) whereas in other cases it indicates predictions invariant across items (Haberman, Sinharay, & Lee, 2011;Wainer, 2016). 4 In simulation settings, we will generate new data for testing purposes from the known data generating model.…”

Section: The Imv For Irt Models With Dichotomous Outcomesmentioning

confidence: 99%

The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items

Domingue,

Kanopka,

Kapoor

et al. 2024

Psychometrika

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The deployment of statistical models, such as those used in item response theory (IRT), necessitates the use of indices that are informative about the degree to which a given model is appropriate for a specific data context. We introduce the InterModel Vigorish (IMV) as an index that can be used to quantify accuracy for models of dichotomous item responses based on the improvement across two sets of predictions (i.e., predictions from two item response models or predictions from a single such model relative to prediction based on the mean). This index has a range of desirable features: it can be used for the comparison of non-nested models and its values are highly portable and generalizable. We use this fact to compare predictive performance across a variety of simulated data contexts and also demonstrate qualitative differences in behavior between the IMV and other common indices (e.g., the AIC and RMSEA). We also illustrate the utility of the IMV in empirical applications with data from 89 dichotomous item response datasets. These empirical applications help illustrate how the IMV can be used in practice and substantiate our claims regarding various aspects of model performance. These findings indicate that the IMV may be a useful indicator in psychometrics especially as it allows for easy comparison of predictions across a variety of contexts.

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Section: The Imv For Irt Models With Dichotomous Outcomesmentioning

confidence: 99%

The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items

Domingue,

Kanopka,

Kapoor

et al. 2024

Psychometrika

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“…The program learns how quickly you forget information. Due to this, the internal repetition system has high effectiveness in the training of the learners, as it enables them to be trained at the right intervals so that the knowledge is shifted from their short-term memory to their long-term memory [13]. This enables organizations to make their learners proficient in various skills and enhance their performance.…”

Section: -2-internal Repetition Systemmentioning

confidence: 99%

A Digital Model of Full-Cycle Training Based on the Zettelkasten and Interval Repetition System

et al. 2023

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The study aims to propose and validate a new digital model based on the Zettelkasten and interval repetition system for comprehensive and full-cycle training of the students. The core idea is to enhance the learning experience and effectiveness of the given training course by enhancing the student's attention during the learning and long-term information retention. In pursuit of the aforementioned research aim, the study incorporates a quantitative research methodology by combining experiments and a survey. In particular, the effectiveness of the proposed model was assessed via an achievement assessment involving two groups of students and assessing their scores on the same test. This was followed by a metacognitive awareness survey of the two groups to investigate their perceived understanding and performance (with and without the use of a model). The proposed model was found to be effective in enhancing the learning experience and effectiveness of the students on the training course. The Zettelkasten facilitates the management of the student's attention, while the interval repetition system contributes to increased retention. The students that used this model in their learning and preparation scored better than their peers. Also, they reported a significantly higher understanding and awareness of their learning than their peers. The model can be incorporated into the learning process or the provision of training courses to the students. This study is the first to suggest the integration of Zettelkasten and the interval repetition system into one learning model for the students. The article proposes a practical model that can be incorporated by teachers to improve the learning effectiveness of their students. This article has some limitations as well that must be acknowledged. Doi: 10.28991/ESJ-2023-SIED2-01 Full Text: PDF

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“…where effect stands for the forgetting module output, d k is the dimension of keys, and * and T represent multiply and transpose operations, respectively. According to the relevant cognitive science study in [42], we design exponential decay attention to measure the forgetting effect. To weigh the importance of questions in combination with the inevitable forgetting, we consider two critical elements: context-aware distance d(t, τ) [4] and question difficulty parameter θ.…”

Section: Forgetting-fusion Transformermentioning

confidence: 99%

DCKT: A Novel Dual-Centric Learning Model for Knowledge Tracing

et al. 2022

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Knowledge tracing (KT), aiming to model learners’ mastery of a concept based on their historical learning records, has received extensive attention due to its great potential in realizing personalized learning in intelligent tutoring systems. However, most existing KT methods focus on a single aspect of knowledge or learner, not paying careful attention to the coupling influence of knowledge and learner characteristics. To fill this gap, in this paper, we explore a new paradigm for the KT task by exploiting the coupling influence of knowledge and learner. A novel model called Dual-Centric Knowledge Tracing (DCKT) is proposed to model knowledge states through two joint tasks of knowledge modeling and learner modeling. In particular, we first generate concept embeddings in abundant knowledge structure information via a pretext task (knowledge-centric): unsupervised graph representation learning. Then, we deeply measure learners’ prior knowledge the knowledge-enhanced representations and three predefined educational priors for discriminative feature enhancement. Furthermore, we design a forgetting-fusion transformer (learner-centric) to simulate the declining trend of learners’ knowledge proficiency over time, representing the common forgetting phenomenon. Extensive experiments were conducted on four public datasets, and the results demonstrate that DCKT could achieve better knowledge tracing results over all datasets via a dual-centric modeling process. Additionally, DCKT can learn meaningful question embeddings automatically without manual annotations. Our work indicates a potential future research direction for personalized learner modeling, which is of both accuracy and high interpretability.

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Unbounded Human Learning

Cited by 37 publications

References 12 publications

The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items

The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items

A Digital Model of Full-Cycle Training Based on the Zettelkasten and Interval Repetition System

DCKT: A Novel Dual-Centric Learning Model for Knowledge Tracing

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