Machine Learning Bias in Predicting High School Grades: A Knowledge Perspective

Abstract

This study focuses on the machine learning bias when predicting teacher grades. The experimental phase consists of predicting the student grades of 11th and 12thgrade Portuguese high school grades and computing the bias and variance decomposition. In the base implementation, only the academic achievement critical factors are considered. In the second implementation, the preceding year’s grade is appended as an input variable. The machine learning algorithms in use are random forest, support vector machine, and extreme boosting machine. The reasons behind the poor performance of the machine learning algorithms are either the input space poor preciseness or the lack of a sound record of student performance. We introduce the new concept of knowledge bias and a new predictive model classification. Precision education would reduce bias by providing low-bias intensive-knowledge models. To avoid bias, it is not necessary to add knowledge to the input space. Low-bias extensive-knowledge models are achievable simply by appending the student’s earlier performance record to the model. The low-bias intensive-knowledge learning models promoted by precision education are suited to designing new policies and actions toward academic attainments. If the aim is solely prediction, deciding for a low bias knowledge-extensive model can be appropriate and correct. Doi: 10.28991/esj-2021-01298 Full Text: PDF

Mathematics and Mother Tongue Academic Achievement: A Machine Learning Approach

“…There are a few studies using data that do not come from samples. One example is provided by Costa-Mendes et al [61], who used neural networks, support vector machines, regression, random forests, and an extreme gradient boosting machine, discovering that the last yields the best performance. Another example is given by Cruz-Jesus et al [62], who compared the performance of a classic method with ML techniques to understand the factors leading to the failure or success of a school year.…”

Deep Learning in Predicting High School Grades: A Quantum Space of Representation

“…The feature selection procedure solved multicollinearity problems concerning the variables measured for the guardian, the parents simultaneously, and the socioeconomic variables retrieved from Statistics Portugal. On the other hand, the regularization techniques such as dropout and batch normalization had only a minor rule in the deep MLP hyper-tuning optimization, seemingly coherent with the high-bias knowledge-intensive model in question and an inherent low variance trait [6].…”

“…Normally, the LA/EDM learning systems resort to socio-demographic variables, digital log data, and course assignment scores to anticipate the students' AA. They are extensive knowledge models appropriate for predictive but not explanatory analysis [6]. It has also been proved that ANN performs among the best when predicting grades [44].…”

“…On the other hand, EDM and LA develop extensive knowledge models suitable for predictive analysis alone. These models do not have the traditional explanatory nature built upon the measurement of the literature-based AA determinants [6]. To the best of the authors' knowledge, there is no educational econometrics study that has considered deep learning as the explanatory quantitative method.…”

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