An MRI Study on Effects of Math Education on Brain Development Using Multi-Instance Contrastive Learning

This paper explores whether mathematical education has effects on brain development from the perspective of brain MRIs. While biochemical changes in the left middle front gyrus region of the brain have been investigated, we proposed to classify students by using MRIs from the intraparietal sulcus (IPS) region that was left untouched in the previous study. On the cropped IPS regions, the proposed model developed popular contrastive learning (CL) to solve the problem of multi-instance representation learning. The resulted data representations were then fed into a linear neural network to identify whether students were in the math group or the non-math group. Experiments were conducted on 123 adolescent students, including 72 math students and 51 non-math students. The proposed model achieved an accuracy of 90.24 % for student classification, gaining more than 5% improvements compared to the classical CL frame. Our study provides not only a multi-instance extension to CL and but also an MRI insight into the impact of mathematical studying on brain development.

The Influence of Modern Electronic Media on the Teaching Content of Traditional Design

Objectives: At present, most of the researches on differentiated teaching focus on the theoretical research of differentiated instruction, the demonstration of necessity of differentiated instruction, the implementation process of differential teaching, and the implementation of differentiated teaching. Methods: at present, most of the researches on differentiated teaching focus on the theoretical research of differentiated instruction, the demonstration of necessity of differentiated instruction, the implementation process of differential teaching, and the implementation of differentiated teaching. Results: This paper took computer design course as an example, mainly from three aspects: the simple Bias classification algorithm, the student’s difference performance and the difference teaching. Based on the related theories, a student classification method based on Naive Bayes algorithm was proposed. Conclusion: This paper took computer design course as an example, mainly from three aspects: the simple Bias classification algorithm, the student’s difference performance and the difference teaching. Based on the related theories, a student classification method based on Naive Bayes algorithm was proposed.

Answering the Call for Title V Data: A Success Skills Intervention to Increase Retention at a Hispanic-Serving Institution

Research has yielded much evidence that investing in postsecondary interventions increases retention and success for Hispanic/Latinx undergraduates. This study examines one such intervention, funded by Title V and implemented at a large public Hispanic-Serving Institution, developed to improve semester-to-semester retention. Faculty and peer mentors facilitated a set of workshops for probationary students and students in a core university course that connected self-regulatory skills for college success to those for career success. Students participating in the workshop intervention were compared to a control group of students who did not participate. Findings showed that in comparison to the control group, students in the intervention had higher retention rates, regardless of probationary status or student classification, along with higher rates among students with lower GPAs. Implications of the study are discussed in relation to “servingness” at HSIs.

Developing a System for Teaching Effectiveness by Student Classification of Teacher Attributes at the University of Lahore College for Women University

As teaching effectiveness is crucial for achieving academic excellence, teachers' attributes contributing towards teaching effectiveness are worth exploring. This study examines 300 BS. Education and Economics students' perception of teachers' characteristics who have taught them. Accordingly, teachers are categorized based on scores of attributes obtained through student ratings. Association between teacher attributes and overall teaching effectiveness is found, and finally, a teaching effectiveness framework is designed based on characteristics, which were significantly associated with teaching effectiveness. The majority (>60%) of students rated all attributes under the medium category, with 54.64% and 50.61% of students placing (rating) overall teaching effectiveness under the high and medium sort respectively, with 17.61 % under the low category. Also, all attributes were found to be positively correlated with overall teaching effectiveness. Out of 30 items under all attributes, 22 items significantly associated with teaching effectiveness were included in the teaching effectiveness framework. In light of the findings, we give teachers suggestions regarding their teaching attributes as perceived by students.

Educational analytics. A new approach to improving the quality of the educational process

This article describes the trends in online education caused by the COVID-19 pandemic. The introduction of learning analytics into the educational process is substantiated. The main methods and tools of educational analytics are considered. Using a specific example, we will understand the construction and assessment of a student classification model using the high-level programming language Python.

Enhanced CNN Models for Binary and Multiclass Student Classification on Temporal Educational Data at the Program Level

In educational data mining, student classification is an important and popular task by predicting final study status of each student. In the existing works, this task has been considered in many various contexts at both course and program levels with different learning approaches. However, its real-world characteristics such as temporal aspects, data imbalance, data overlapping, and data shortage with sparseness have not yet been fully investigated. Making the most of deep learning, our work is the first one addressing those challenges for the program-level student classification task. In a simple but effective manner, convolutional neural networks (CNNs) are proposed to exploit their well-known advantages on images for temporal educational data. As a result, the task is resolved by our enhanced CNN models with more effectiveness and practicability on real datasets. Our CNN models outperform other traditional models and their various variants on a consistent basis for program-level student classification.

A typology of student diversity and an inclusive student learning support system: Insights for higher education

Higher education today is characterized by a highly diversified student population. This, in turn, calls for greater inclusivity in higher education. To answer this call, this paper introduces a typology of student diversity in, and an inclusive student learning support system for, higher education. More specifically, the typology of student diversity suggests that students in higher education may comprise of adults, school leavers, indigenous students, low socio-economic background students, and international students. The typology explains each student classification in detail using five relevant factors, namely autonomy, anxiety, motivation, discipline, and life experience. Finally, the paper offers some pertinent insights to create an inclusive student learning support system for higher education based on the insights derived from the typology of student diversity in higher education.