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Sgp Application Prediction
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By Yupei Zhang Yupei Zhang Scilit Preprints.org Google Scholar View Publication , Yu Yu Yun Scilit Preprints. Org Google Scholar View Publication and Xuequn Shang Xuequn Shang Scilit Preprints.org Google Scholar View Publication *
Received: 9 January 2020 / Revised: 18 February 2020 / Accepted: 21 February 2020 / Published: 4 March 2020
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Student grade prediction (SGP) is an important educational problem for designing personalized teaching and learning strategies. Most studies adopt matrix factorization (MF) techniques. However, their methods often focus on value registers without paying attention to peripheral information such as background and relationships. To achieve this goal, in this paper we propose a new MF method called Graph Regularized Strong Matrix Factorization (GRMF) based on the latest version of strong MF. GRMF synthesizes two side graphs built on student and course data into a robust low-rank MF objective. As a result, student and course learning characteristics may capture greater preferences about educational status to achieve higher grade prediction outcomes. The resulting objective problem can be effectively optimized using the maximization minimization (MM) algorithm. Furthermore, GRMF can not only build specific features for the education domain, but also deal with missing, noisy, and corrupt data. To validate our method, we tested GRMF on two common datasets to obtain rank predictions and images. Finally, we apply the GRMF to the education data of our university, which consists of 1,325 students and 832 courses. Extensive experimental results clearly show that GRMF can overcome various data problems and achieve more effective features than other methods. In addition, GRMF also provides higher prediction accuracy than other methods on our educational dataset. This technology can facilitate personalized teaching and learning in higher education.
In secondary school education, Student Grade Prediction (SGP) is very useful to help all stakeholders in the education process. For students, SGP helps them choose appropriate subjects or exercises to improve their knowledge and make basic plans for the semester. For instructors, SGP can help customize learning materials and teaching programs based on students’ abilities and identify incompetent students during course progression. For education managers, SGP will help them to examine curriculum programs and organize courses in scientific order. All stakeholders in the education process can develop better independent plans to improve educational outcomes and achieve higher graduation rates. SGP is an important issue for science education in the field of STEM (Science, Technology, Engineering and Mathematics) and mentioned in the work of G. Shannon et al. [1].
Student Grade Prediction aims to predict the final grades/course grades of the target students in the next semester. The SGP provides a useful reference for early evaluation of educational outcomes, and is therefore essential for various tasks in personalized education, such as ensuring timely graduation [2] and learning value [3, 4]. In the past few years, many studies have focused on SGP and several methods have been developed [5].
In principle, existing methods can be divided into three categories according to their formulation as follows: (1) problem classification. SGP is reconstructed by labeling target students with predefined value tags and solving them by classification models such as decision trees [ 6 ], logic regression [ 7 , 8 ], and support vector machines [ 9 , 10 ]. (2) The lag problem. Taking grades as response variables, SGP is rewritten to score by student or subject characteristics, such as linear regression [5, 11, 12], neural networks [13, 14, 15], and random forests [9]. (3) Completion of the Matrix. Since grade records can be poured into a matrix, SGP has also been formulated as a predictor of missing grades from a student-subject matrix when each element is a course grade [16]. This formulation is usually solved by popular matrix factorization methods, which have been widely studied and resulted in many effective approaches [17, 18]. Specifically, based on the same dataset, Tai-Nge et al. A comparison of matrix competition with traditional regression methods such as logistic/linear regression and experimental results shows that matrix competition can improve prediction results [ 19 ].
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MF-based methods aim to learn latent features of students and subjects from given grade data and use these features for SGP [ 20 ]. Here we review related works using MF techniques. Traditional MF uses “slip rate” (probability of making mistakes even if students know how to solve the question) and “guess rate” (probability of students guessing even if students don’t know how to solve the question). ) OK) students tested and performed well on the 2010 KDD (Knowledge Discovery and Data Mining) Trophy educational dataset [ 21 ]. In references [22, 23], non-negative matrix factorization (NMF) is used to combine non-negative student series. Due to the increased ability of students, tensor factorization (TF) is used for transient effects in reference [24]. Since the value matrix is implicitly a low-rank matrix, low-rank matrix factorization (LRMF) is investigated on a dataset from an online learning platform in the work of Lorenzen et al. [25]. However, existing MF-based methods often face problems such as missing data, corrupted data, and data intercepts. In particular, they fail to consider peripheral information contained in other useful educational data, such as background data and data on daily behavior at school.
Criteria for improving immunity [26, 27, 28]. Also, in real-world applications we often have a large amount of side information data available. Rao et al. proposed a Graph Regularized Recursive Least Squares (GRALS) method to synthesize two graphs of movie and viewer side information data [29]. More specifically, in the real context of higher education, the datasets usually have the following properties: (1) the grade matrix is lost in course selection and is corrupted by some human factors. (2) Students from the same background tend to perform similarly in courses [30]. For example, two students have more practice in computer programming, so both can get better grades with higher probability in their C language course. (3) Courses of equal knowledge result in equal grades for a student. For example, C is similar to data structures, while C is not similar to history, so students proficient in C will do well in data structures, but not necessarily history.
To this end, we propose a new MF method called dual graph regularized robust matrix factorization (GRMF), and then apply GRMF to SGP as shown in Figure 1 . GRMF integrates only the robust loss function of RMF-MM. The graph generates two-dimensional information using student and course background data. The MM algorithm can effectively solve the resulting optimization problem. The multiple contributions of our article are summarized as follows:
The remainder of this paper is organized as follows – In Section 2, we formulate the SGP problem, followed by a brief overview of MF techniques. We present in Section 3 and the GRMF algorithm in Section 4. Section 5 shows experimental results on movie speed prediction, image restoration and SGP. Section 6 concludes this article.
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In this section, we formulate the SGP problem in mathematical form and then present a promising matrix factorization technique.
In today’s higher education at universities, teachers provide a “one-size-fits-all” curriculum and students
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