Social-Emotional Learning Iterations to Strengthen Programs in India for Gifted Students in Poverty

A nonresidential gifted program for economically disadvantaged students in India (Grades 6 through 12) uses a continuum of services for social emotional learning (SEL) support to prepare students for college admission. The program stands in contrast to the residential gifted schools in India, which have minimal SEL considerations. SEL is deeply integrated with the Sitare Foundation program's design and evaluation by using emotional intelligence assessments and action plans to customize support for its students and staff. During the coronavirus pandemic, SEL training and mentoring of the city coordinators provided resilience models to encourage continued commitment to the program, especially for female gifted students. Three specific examples (student, leader, and coordinators) are presented as illustrations of growth and transformation. Continuous gathering of both qualitative and quantitative SEL data, combined with traditional academic records, is recommended for effective program iterations.

Verifiable Authentication and Issuance of Academic Certificates using Permissioned Blockchain Network

Fake certificates pose a severe problem in today's world; they vouch for an individual's false skillset and put an organization's reputation at risk. Moreover, the existing verification process is performed in a centralized manner, often too cumbersome and time-consuming to the end-user, lacking transparency in the educational institutions' Issuance of certificates. Of-late, blockchain is a promising technology that provides transparent, secure, and reliable features, which offers solutions to the education sector. This paper provides the solution to the educational certification problem by employing the blockchain network. We proposed a permissioned blockchain network that identifies, authenticates the Issuer, adequate verification, securely shares academic records to the recipients, and stores the certificate credentials in the blockchain in a distributed manner.

Course Crediting and Academic Evaluation System of The College of Communication and Information Technology

The study aimed to develop a Course Crediting and Academic Evaluation System of the College of Communication and Information Technology (CCIT) in President Ramon Magsaysay State University – Iba Campus to offer an online system to ease the procedures of crediting and evaluation of the student academic records. Descriptive research design and descriptive statistics were utilized in this study. The dean, program chairpersons, and students of Bachelor of Science in Computer Science, Bachelor of Science in Computer Engineering and Bachelor of Science in Computer Engineering are the respondents of the study. The findings revealed that the respondents evaluated the software quality of the system using the International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC): 25010:2011 as excellent. The respondents evaluated as strongly recommended on the degree of recommendation of the acquisition and implementation of the system. The Course Crediting and Academic Evaluation System is recommended to implement to improve the present procedures. Training to the end-users is advised to be conducted to know how to use the system. The maintenance and continuous enhancement of the system to adapt to the changing trends in information technology.

Students’ Class Performance Prediction Using Machine Learning Classifiers

Nowadays, educational data mining is being employed as assessing tool for study and analysis of hidden patterns in academic databases which can be used to predict student’s academic performance. This paper implements various machine learning classification techniques on students’ academic records for results predication. For this purpose, data of MS(CS) students were collected from a public university of Pakistan through their assignments, quizzes, and sessional marks. The WEKA data mining tool has been used for performing all experiments namely, data pre-processing, classification, and visualization. For performance measure, classifier models were trained with 3- and 10-fold cross validation methods to evaluate classifiers' accuracy. The results show that bagging classifier combined with support vector machines outperform other classifiers in terms of accuracy, precision, recall, and F-measure score. The obtained outcomes confirm that our research provides significant contribution in prediction of students’ academic performance which can ultimately be used to assists faculty members to focus low grades students in improving their academic records.

CV Ranking System and Portfolio Detection

In recent times there is an increase in population and since of that plenty of individuals apply for one position of job. For this every single applicant provides his/her CV which contains its biodata, academic records and skill set. Within the present times the human resource department has to manually undergo all the CVs then they call the eligible candidates for an interview. Even after this much of human effort this method isn’t efficient and involves manual reading of documents (CVs). Because of this the human resource requires many HR officers and plenty of their time. In this project we'll atomate this process with the assistance of web technologies and machine learning algorithms so we should not depend on humans for ranking the CVs and try this process in more efficient and faster way.

HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

MIDFIELD dataset is a unit-record longitudinal dataset for undergraduate students from 16 universities. MIDFIELD contains all the information that appears in students' academic records, including demographic data (sex, age, and race/ethnicity) and information about major, enrollment, graduation, and school and pre-school performance.

HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

MIDFIELD dataset is a unit-record longitudinal dataset for undergraduate students from 16 universities. MIDFIELD contains all the information that appears in students' academic records, including demographic data (sex, age, and race/ethnicity) and information about major, enrollment, graduation, and school and pre-school performance.