Designing and Implementing e-School Systems: An Information Systems Approach to School Management of a Community College in Northern Mindanao, Philippines

Purpose–Colleges and Universities have been established to provide educational services to the people. Like any other organization, the school has processes and procedures similar to business or industry that involve admissions, processing of data, and generation of reports. Those processes are made possible through a centralized system in storing, processing, and retrieval of data and information, the majority of the schools in the country are already adopting computer-based systems to address their needs especially on their student and school-related transactions. The absence of a computer system and the complexity of the transactions of the college which makes the personnel be loaded with paper works in storingand keeping student records and information is the motivating factor why the School Management Information System has been designed and developed for a community college in the northern part of Mindanao.Method-This paper discusses the Major Functionalities and Modules of the systemthroughits implementation methodology which is the AgileModel and its impact on the delivery of services and procedures in the overall operation of the college.Results–The project has been evaluated based on ISO 25010,a quality model used for product/software quality evaluation systems. Based on the results of the evaluation, SMIS has been Functional, Usable, and Reliable with an average for every criterion above 4.04indicating very good performance based on a Likert scale descriptive interpretation.Conclusion–Based on the preceding findings of the study, the respondents agreed that the developed e-school system was functional and lifted the transaction process of the school. The faculty and staff have benefited from making use of the system. The overall quality and performance of the system was verygood in terms of functionality, usability, andreliability.Recommendations–It is recommended that future development such as the smartphone and tablet-based attendance monitoring should be integrated, a kiosk for grades and schedule viewing should also be placed inside the campus that is connected to the database server. Online student information systems should also be developed for the benefit of the students and parents, in easily monitoring school-related activities and requirements.Research Implications–The study enabled the centralization of school and student data in storing, processing and retrieval. The System has been implemented in the college and has been updated now and then for continuous quality improvement.

Teacher Mindsets Help Explain Where a Growth-Mindset Intervention Does and Doesn’t Work

A growth-mindset intervention teaches the belief that intellectual abilities can be developed. Where does the intervention work best? Prior research examined school-level moderators using data from the National Study of Learning Mindsets (NSLM), which delivered a short growth-mindset intervention during the first year of high school. In the present research, we used data from the NSLM to examine moderation by teachers’ mindsets and answer a new question: Can students independently implement their growth mindsets in virtually any classroom culture, or must students’ growth mindsets be supported by their teacher’s own growth mindsets (i.e., the mindset-plus-supportive-context hypothesis)? The present analysis (9,167 student records matched with 223 math teachers) supported the latter hypothesis. This result stood up to potentially confounding teacher factors and to a conservative Bayesian analysis. Thus, sustaining growth-mindset effects may require contextual supports that allow the proffered beliefs to take root and flourish.

Distance Learning During the First Lockdown: Impact on the Family and Its Effect on Students' Engagement

This contribute investigates how Emergency Remote Education (ERE) impacted families during the spring 2020 Covid-19 lockdown, and in particular, the extent to which the impact of ERE on families, measured in terms of space and equipment sharing, moderates the effect of student and family characteristics on students' engagement. The study derived from the administration of an online survey to 19,527 families with children attending schools, from nursery to upper secondary grade. The total number of student records collected amounted to 31,805, since parents had to provide data for each school-age child in the family. The survey contains 58 questions, divided into three sections, with the first two sections designed to get a reading at family level and the third section to gather data for each school-age child in the family. After verifying the validity of the engagement construct through confirmatory factor analysis, two structural equation models were used to analyze the students' engagement. The main findings reveal how the impact of the ERE on the families has had a significant role in predicting students' level of engagement observed by parents with respect to different predictor variables. Finally, we argue that it is necessary to follow a holistic approach to observe the challenges imposed by the switch of the process of deferring teaching from presence to distance, imposed by the pandemic emergency on families. In fact, a holistic approach can promote student engagement and prevent the onset of cognitive-behavioral and affective problems linked to disengagement in ERE.

Supervised and Unsupervised Data Mining Techniques on Employability of Public Higher Learning Institute Graduates in Malaysia

It is the students’ dream to secure a job right after graduation. However, there are factors that hinder their employability. This study aims to predict Malaysian graduates’ employment status based on employability factors and to profile the graduates’ satisfaction towards their curricular activities and information and communications technology (ICT) skills. A total of 375,507 student records were obtained based on tracer studies conducted by the Malaysian Ministry of Higher Education between 2015 and 2018. Due to the large amount of data with various categories, supervised and unsupervised data mining techniques were used to unmask the underlying variables and reveal hidden information about graduates’ employability for better tracing the employment status of graduates. Various types of consolidation techniques were also used to reduce the number of levels for categorical inputs in the dataset, namely, classifiers without consolidation, with manual consolidation, and with tree consolidation. Three types of data mining variable selections were used to improve the performance of the classifiers in predicting employment status. The results show that logistic regression (LR) without variable selection is the best classifier for data without consolidation, while LR using variable selection with LR stepwise is the best classifier for data with manual and tree consolidations. In profiling the satisfaction of graduates, K-Means Clustering was used, which revealed seven clusters. The most prominent cluster consisted of graduates who were highly satisfied with their ICT skills but less satisfied with their curricular activities. These data mining techniques were able to trace graduates’ employment status and identify the success factors of graduates’ employability.

Dataset of Students’ Performance Using Student Information System, Moodle and the Mobile Application “eDify”

The data presented in this article comprise an educational dataset collected from the student information system (SIS), the learning management system (LMS) called Moodle, and video interactions from the mobile application called “eDify.” The dataset, from the higher educational institution (HEI) in Sultanate of Oman, comprises five modules of data from Spring 2017 to Spring 2021. The dataset consists of 326 student records with 40 features in total, including the students’ academic information from SIS (which has 24 features), the students’ activities performed on Moodle within and outside the campus (comprising 10 features), and the students’ video interactions collected from eDify (consisting of six features). The dataset is useful for researchers who want to explore students’ academic performance in online learning environments, and will help them to model their educational datamining models. Moreover, it can serve as an input for predicting students’ academic performance within the module for educational datamining and learning analytics. Furthermore, researchers are highly recommended to refer to the original papers for more details.

A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course

The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in predicting their later results; which is useful, for instance, for an early intervention. With this aim, we propose a methodology based on the random Tukey depth and a non-parametric kernel. This methodology allows teachers and evaluators to define the variables that they consider most appropriate to measure those aspects related to the academic performance of students. The methodology is applied to a real case study obtaining a success rate in the predictions of over the 80%. The case study was carried out in the field of Human-computer Interaction.The results indicate that the methodology could be of special interest to develop software systems that process the data generated by computer-supported learning systems and to warn the teacher of the need to adopt intervention mechanisms when low academic performance is predicted.

Placement Prediction System

Placement prediction system is a useful software for managers and students. An educational institution contains student records which is a wealth of information but is very large one person analyzes complete student records. To find out the placement status of each student at institution is a tedious task. Therefore, the limit of the system includes the use of time, which is minimal efficient and with little user satisfaction. The project implementation prediction plan predicts the reader placement using a variety of machine learning methods such as merging methods, regression strategies, decision solution etc. Based on student schools with the ability to measure, English skill, logical ability, technical personality testing. Improved model used predict the placement of students in the training and placement office (TPO)

College Management System

This paper is aimed at developing an Online Application for students and college management. The main theme of the project is to develop an application that enables the students to perform the activities like fee payment, downloading learning materials, and get notifications about the attendance, results, etc; in a mobile application, It provides a simple interface for the maintenance of the student records. In the previous system, all the details have to view in a file or on a website. At the same time while searching for information it takes a long time to get the results. To overcome this an Android Application can be used to make this process much better. All the important data such as passwords are securely stored with the help of hashing techniques. The users of this application are students, Faculty, and Admin. The Faculty can log in to the application and can update the Attendance and Marks of the student. In case of any events or exams in the college, the students will be notified by Admin. As we are college students we know about these problems that we are facing due to the lack of this kind of application. So we thought of extending and solving some of the problems in our project so that it could be more useful for the students and they can easily use the application which is also very compatible for the teachers and students. The data relating to students is stored in Firebase. A web-based portal is designed for the faculty to upload the marks, results, and upload materials.