Automated Academic and Professional Behaviors Student Tracking Systems (original) (raw)

DAWN IRIS CALIBO et al: Improving Educational Quality Integration of a Graduate Tracer Automated System for an Academe

Turkish Online Journal of Educational Technology, 2017

Technological advancement has offered the field of instruction more practical tools to upgrade quality in education. This tool includes the capability of tracking down graduates as team players in the real world of employment to assess if the educational institution has provided them with the necessary training and skills needed to become employable graduates. In this study, quality and effectiveness of the graduate tracking tool are assessed for the assurance of quality outcome from the system that leads to better decisions on future developments. Using a research questionnaire based on published literature on the information system, the researcher determined the quality of the existing graduate tracer system for the college. The study reveals that the entities such as System Quality, Information Quality, and Service Quality contribute to the effectiveness based on the system’s quality. Thus, with the data on hand, the findings show that there is user satisfaction that makes it effective for the state college to integrate the alumni tracer system.

Automatic Student Performance Analysis and Monitoring

International Journal of Innovative Research in Computer and Communication Engineering, 2016

This paper presents the survey of work done in existing systems for student performance analysis and monitoring. As well as the survey to understand and analyze the existing system and the algorithms that are used in it and to propose a system that will analyze student performance and will guide them by displaying the areas where they need improvement, in order to contribute to a student's overall development by generating a score card for the same. This paper presents the analysis of student performance on the basis of academic performance, research and innovation, self-development and extra-curricular activities.

Academic advisors‘ record keeping andmonitoring system (AA-ReKeMoS) / Nik Zam Nik Wan ...[et al.]

2019

UiTM Academic Advisors or commonly known as 'Penasihat Akademik (PA)' are appointed among lecturers to be responsible for a group of students. PA is responsible to advice those students with regards to their academic planning, to monitor their academic activities as well as academic achievements. PA can play an important role in ensuring the students meet UiTM's academic objectives which include graduating on time with CGPA more than 3.00. However, due to demanding schedule of PA as lecturers, they seem to be having lack of time to schedule meetings with the students and unsystematic recording of PA-students meetings will make monitoring almost impossible. Consequently problematic as well as weak students cannot be identified sooner for appropriate actions leading to poor academic performance that would affects faculty's overall performances. Therefore, Academic Advisors' Record Keeping and Monitoring System (AA-ReKeMoS) is necessary to enable PA-students' activities to be frequently updated and the information can be made accessible to Head of Faculty for monitoring purposes. Apart from monitoring individual PA's activities, Head of Faculty can also obtain information about each student from the system. AA-ReKeMoS is cheap, manageable and can be widely applied since it utilized Microsoft Excel and Google Drive that can be easily accessed and are free for all the Academic Advisors.

Assisting Educational Analytics with AutoML Functionalities

Computers

The plethora of changes that have taken place in policy formulations on higher education in recent years in Greece has led to unification, the abolition of departments or technological educational institutions (TEI) and mergers at universities. As a result, many students are required to complete their studies in departments of the abolished TEI. Dropout or a delay in graduation is a significant problem that results from newly joined students at the university, in addition to the provision of studies. There are various reasons for this, with student performance during studies being one of the major contributing factors. This study was aimed at predicting the time required for weak students to pass their courses so as to allow the university to develop strategic programs that will help them improve performance and graduate in time. This paper presents various components of educational data mining incorporating a new state-of-the-art strategy, called AutoML, which is used to find the b...

Development and Application of Academic Analytics in a School

2020

This document relates the data analysis that comes from academic thinking, as well as the data collection that generates potential tools. There is a growing interest in data mining and educational systems, which makes educational data mining a new growing research community, allowing the opportunity to track and store student learning activities as large data sets in online environments, making us aware of an intelligent use of the data produced by the academic environment, which allows us understand and predict the processes involved, as well as optimize the environments in which such learning occurs.

Intelligently Raising Academic Performance Alerts

2008

Abstract. We use decision trees and genetic algorithms to analyze the academic performance of students and the homogeneity of tutoring teams in the undergraduate program on Informatics at the Hellenic Open University (HOU). Based on the accuracy of the generated rules, we examine the applicability of the techniques at large and reflect on how one can deploy such techniques in academic performance alert systems.

Introduction to the Special Issue: Toward an Explicit Technology for Generalizing Academic Behavior

Journal of Behavioral Education, 2010

This special issue of the Journal of Behavioral Education was designed to call attention to a much needed area of academic intervention research: generalization programming. Although the occurrence of generalized responding across items, settings, tasks, and time is clearly recognized as a goal of intervention, less research has been devoted to the technology through which such generalization may occur. This

Model for the collection and analysis of data from teachers and students supported by Academic Analytics

Procedia Computer Science, 2020

Academic Analytics enables an analysis of data that is very important for making decisions in the educational institutional environment, aggregating valuable information in the academic research activity and providing easy to use business intelligence tools. This article shows a proposal for creating an information system based on Academic Analytics, designing a model that is supported by Academic Analytics for the collection and analysis of data from the information systems of educational institutions. The idea that was conceived proposes a system that is capable of displaying statistics on the historical data of students and teachers taken over academic periods, with the purpose of gathering the information that the director, the teacher, and finally the student need for making decisions. The model was validated with information taken from students and teachers during the last five years, and the export format of the data was pdf, csv, and xls files. The findings allow us to state that it is extremely important to analyze the data that is in the information systems of the educational institutions for making decisions. After the validation of the model, it was established that it is a must for students to know the reports of their academic performance in order to carry out a process of self-evaluation, as well as for teachers to be able to see the results of the data obtained in order to carry out processes of sel fevaluation, and adaptation of content and dynamics in the classrooms.

An artificial intelligence approach to monitor student performance and devise preventive measures

Smart Learning Environments, 2021

A major problem an instructor experiences is the systematic monitoring of students’ academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to collect enormous amount of data concerning their students from various sources, however, the institutes are craving novel procedures to utilize the data to magnify their prestige and improve the education quality. This research evaluates the effectiveness of machine learning algorithms to monitor students’ academic progress and informs the instructor about the students at the risk of ending up with unsatisfactory result in a course. In addition, the prediction model is transformed into a clear shape to make it easy for the instructor to prepare the necessary precautionary procedures. We developed a set of prediction models with distinct machin...

Advising at Scale: Automated Guidance of the Role Players Influencing Student Success

The AIR professional file, 2023

Although student advising is known to improve student success, its application is often inadequate in institutions that are resource constrained. Given recent advances in large language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT), automated approaches such as the AutoScholar Advisor system affords viable alternatives to conventional modes of advising at scale. This article focuses on the AutoScholar Advisor system, a system that continuously analyzes data using modern methods from the fields of artificial intelligence (AI), data science, and statistics. The system connects to institutional records, evaluates a student's progression, and generates advice accordingly. In addition to serving large numbers of students, the term "advising at scale" refers to the various role players: the executives (whole-institution level), academic program managers (faculty and discipline levels), student advisors (faculty level), lecturers (class level), and, of course, the students (student level). The form of advising may also evolve to include gamification elements such as points, badges, and leaderboards to promote student activity levels. Case studies for the integration with academic study content in the form of learning pathways are presented. We therefore conclude with the proposition that the optimal approach to advising is a hybrid between human intervention and automation, where the automation augments human judgment.