Predicting the Students Performance using Regularization-based Linear Regression

Yamasari, Yuni; Rochmawati, Naim; Putra, Ricky Eka; Qoiriah, Anita; Asmunin,; Yustanti, Wiyli

doi:10.1109/icvee54186.2021.9649704

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…On the other hand, several models have been tested for predicting a final score ( Anozie and Junker 2006 ; Bicer et al 2013 ; Namoun and Alshanqiti 2021 ). We consider a linear model for predicting scores, such as the models used by Ulloa Miranda ( 2021 ), Zheng et al ( 2019 ) and Yamasari et al ( 2021 ). For this, a student i is represented with a vector of k regressors

, and its estimated score is defined by:

where the slope

and the intercept b are parameters of the fitted least squares regression with n students, that is:

where each

is the real score obtained by student i .…”

Section: Methodsmentioning

confidence: 99%

Do Written Responses to Open-Ended Questions on Fourth-Grade Online Formative Assessments in Mathematics Help Predict Scores on End-of-Year Standardized Tests?

Urrutia

Araya

2022

J. Intell.

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Predicting long-term student achievement is a critical task for teachers and for educational data mining. However, most of the models do not consider two typical situations in real-life classrooms. The first is that teachers develop their own questions for online formative assessment. Therefore, there are a huge number of possible questions, each of which is answered by only a few students. Second, online formative assessment often involves open-ended questions that students answer in writing. These types of questions in online formative assessment are highly valuable. However, analyzing the responses automatically can be a complex process. In this paper, we address these two challenges. We analyzed 621,575 answers to closed-ended questions and 16,618 answers to open-ended questions by 464 fourth-graders from 24 low socioeconomic status (SES) schools. Using regressors obtained from linguistic features of the answers and an automatic incoherent response classifier, we built a linear model that predicts the score on an end-of-year national standardized test. We found that despite answering 36.4 times fewer open-ended questions than closed questions, including features of the students’ open responses in our model improved our prediction of their end-of-year test scores. To the best of our knowledge, this is the first time that a predictor of end-of-year test scores has been improved by using automatically detected features of answers to open-ended questions on online formative assessments.

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, and its estimated score is defined by:

where the slope

and the intercept b are parameters of the fitted least squares regression with n students, that is:

where each

is the real score obtained by student i .…”

Section: Methodsmentioning

confidence: 99%

Do Written Responses to Open-Ended Questions on Fourth-Grade Online Formative Assessments in Mathematics Help Predict Scores on End-of-Year Standardized Tests?

Urrutia

Araya

2022

J. Intell.

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“…While their context differs from ours, their observation that Elastic net regression provided an optimal balance between bias and variance coincides with our findings in predicting relative humidity. Furthermore, Yamasari et al (2021) also used Regularization-based Linear Regression for predicting student performance. Their positive findings bolster our claim on the efficacy of this approach.…”

Section: ) Comparisons With Previous Researchmentioning

confidence: 99%

Analysis and Prediction of Relative Humidity Level using Generalized Linear Model

Nugroho,

Wicaksono,

Choiruddin

2023

JITE

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The significance of humidity as a critical climate parameter impacts various sectors, including agriculture, health, and energy, necessitating a comprehensive understanding of its influencing factors. This study investigates the influence of climatic variables such as temperature, rainfall, sunshine duration, wind speed, and wind direction on the humidity levels in DKI Jakarta from 2019 to 2022. The objective is to develop a time-independent predictive model for humidity based on historical climate data. The methodology includes data pre-processing to impute missing values and replace outliers, followed by exploratory data analysis to ascertain variable distribution and inter-relationships. A regression model was initially employed for analysis, with subsequent application of regularization via a generalized linear model to enhance prediction accuracy. Results indicate that temperature, rainfall, sunshine duration, and wind direction significantly impact humidity levels in the investigated period. High inter-variable correlation posed challenges of multicollinearity and overfitting in the initial model. However, the application of regularization, trained with 75% of the historical dataset, mitigated these issues and improved model accuracy. This is evident in the improved Mean Squared Error (MSE) performance metrics of the Elastic-Net Regression Model (12.2), compared to the initial Multiple Regression Model (12.5). These findings hold potential implications for weather forecasting and climate change studies

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“…On the other hand, several models have been tested for predicting a final score [40], [39], [48]. We consider a linear model for predicting scores, such as the models used by [36], [35] and [49]. For this a student i is represented with a vector of regressors x i = (x i,0 , x i,1 , ..., x i,k ) and its score is defined by:…”

Section: Questions Answersmentioning

confidence: 99%

Do Written Responses to Open-Ended Questions on Fourth-Grade Formative Assessments in Mathematics Help Predict Scores on End-of-Year Standardized Tests?

Urrutia¹,

Araya²

2022

Preprint

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: Predicting long-term student learning is a critical task for teachers and for educational data mining. However, most of the models do not consider two typical situations in real-life classrooms. The first is that teachers develop their own questions for formative assessment. Therefore, there are a huge number of possible questions, each of which is answered by only a few students. Second, formative assessment often involved open-ended questions that students answer in writing. These types of questions in formative assessment are highly valuable. However, analyzing the responses automatically can be a complex process. In this paper, we address these two challenges. We analyzed 621,575 answers to closed-ended questions and 16,618 answers to open-ended questions by 464 fourth-graders from 24 low-SES schools. We constructed a classifier to detect incoherent responses to open-ended mathematics questions. We then used it in a model to predict scores on an end-of-year national standardized test. We found that despite answering 36.4 times fewer open-ended questions than closed questions, including features of the students’ open responses in our model improved our prediction of their end-of-year test scores. To the best of our knowledge, this is the first time that a predictor of end-of-year test scores has been improved by using automatically detected features of answers to open-ended questions on formative assessments.

show abstract

Predicting the Students Performance using Regularization-based Linear Regression

Cited by 6 publications

References 17 publications

Do Written Responses to Open-Ended Questions on Fourth-Grade Online Formative Assessments in Mathematics Help Predict Scores on End-of-Year Standardized Tests?

Do Written Responses to Open-Ended Questions on Fourth-Grade Online Formative Assessments in Mathematics Help Predict Scores on End-of-Year Standardized Tests?

Analysis and Prediction of Relative Humidity Level using Generalized Linear Model

Do Written Responses to Open-Ended Questions on Fourth-Grade Formative Assessments in Mathematics Help Predict Scores on End-of-Year Standardized Tests?

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