The design and implementation of a customized, web-based learning environment.

by Ka-Po Ma.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 97-105).Abstracts in English and Chinese.Acknowledgments --- p.iiAbstract --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Web-based Education --- p.1Chapter 1.2 --- Customized Web-based Learning --- p.3Chapter 1.3 --- Thesis Overview --- p.6Chapter 2 --- Web-based Education --- p.7Chapter 2.1 --- Impact on Traditional Learning --- p.8Chapter 2.2 --- Theoretical Perspectives on Teaching and Learning --- p.9Chapter 2.2.1 --- Behaviorism Versus Constructivism --- p.10Chapter 2.2.2 --- "Categorization of Individual, Group and Collaborative Learn- ing" --- p.12Chapter 2.3 --- On-line Eduation and Web-based Learning System --- p.15Chapter 2.4 --- Technologies used in Web-based Learning --- p.17Chapter 3 --- General Automated Timetabling --- p.21Chapter 3.1 --- Timtabling Problem --- p.21Chapter 3.2 --- Formulation and Solution Approaches --- p.22Chapter 4 --- "Virtual Campus, Customized Web-based Learning Environment" --- p.25Chapter 4.1 --- Changing Trend in Learning Process --- p.25Chapter 4.2 --- System Design Issue --- p.26Chapter 5 --- System Architecture Issue --- p.31Chapter 5.1 --- Client-server Model --- p.31Chapter 5.1.1 --- Server Side --- p.33Chapter 5.1.2 --- Client Side --- p.34Chapter 5.2 --- Functional-oriented Design --- p.35Chapter 5.3 --- Private Functionality Issue --- p.37Chapter 5.3.1 --- Access Authorizing --- p.37Chapter 5.3.2 --- Availability Updating --- p.40Chapter 5.3.3 --- Personal Information Querying and Modifying --- p.42Chapter 5.3.4 --- Status Selecting --- p.42Chapter 5.3.5 --- Current Online User Querying --- p.43Chapter 5.4 --- Lecture Functionality Issue --- p.44Chapter 5.5 --- Personal Scheduling Functionality Issue --- p.45Chapter 5.6 --- Collaboration Functionality Issue --- p.50Chapter 5.6.1 --- Chatting Room --- p.50Chapter 5.6.2 --- Discussion Board --- p.56Chapter 5.6.3 --- Personal URL-bookmark Keeping and Sharing --- p.57Chapter 6 --- Web-based Learning Scheduler (WL-Scheduler) --- p.59Chapter 6.1 --- "Web-based Customized Timetabling Problem, WCTP" --- p.60Chapter 6.2 --- Solution Approach - Local Search --- p.61Chapter 6.3 --- Algorithm for Approaching Feasible Timetables --- p.63Chapter 6.4 --- Evaluating The Best Timetable --- p.66Chapter 7 --- Multimedia Web Presentation System (MWPS) --- p.67Chapter 7.1 --- Overview --- p.67Chapter 7.2 --- System Components --- p.68Chapter 7.2.1 --- The MWPS Server Machine --- p.69Chapter 7.2.2 --- The MWPS Client Machine --- p.69Chapter 7.2.3 --- The Student Machine --- p.69Chapter 7.3 --- Presentation Flow --- p.69Chapter 7.4 --- Highlighed Features --- p.72Chapter 7.4.1 --- Slides Sequence Capturing --- p.72Chapter 7.4.2 --- Audio/Video Capturing --- p.72Chapter 7.4.3 --- Script-Text On Playback --- p.72Chapter 7.4.4 --- Student Feedbacking --- p.73Chapter 7.4.5 --- White Board Facility --- p.73Chapter 8 --- Illustration via Screen-shots --- p.74Chapter 8.1 --- Login Screen --- p.74Chapter 8.2 --- Functionality provided for Students --- p.75Chapter 8.2.1 --- Personalized Learning Timetable --- p.76Chapter 8.2.2 --- Lecture Delivery --- p.78Chapter 8.2.3 --- Checking active users in Virtual Campus --- p.78Chapter 8.2.4 --- View and Update Personal Information --- p.79Chapter 8.2.5 --- Taking An Entry Test for Interesting Subject --- p.81Chapter 8.2.6 --- Changing Current State --- p.84Chapter 8.2.7 --- Discussion Board --- p.84Chapter 8.2.8 --- Chatting Room --- p.85Chapter 8.3 --- Functionality provided for Teachers --- p.85Chapter 8.4 --- Functionality provided for Administrators --- p.92Chapter 9 --- Conclusion --- p.94Appendix --- p.106Chapter A --- Appendix --- p.106Chapter A.1 --- Internet Technology --- p.106Chapter A.2 --- Web Server --- p.107Chapter A.3 --- Web Client/Server Example --- p.10

Personal Digital Assistants – teachers prefer the personal

This paper will present the results of a small-scale project, funded by the UK Teacher Development Agency, where 13 teachers and 3 trainee teachers in one secondary school science department were given handhelds (Personal Digital Assistants or PDAs) with cameras and internet access for the academic year. The aims were: • to build capacity - enabling trainee teachers to share their mlearning practice; • to enable school based associate tutors to join the elearning community linked to the initial teacher training course and • to encourage reflective practice amongst trainee teachers by enabling access anytime and anywhere to blogs for recording their teaching experiences. However, initial indications are that not all these aims succeeded. The handhelds were viewed as personal devices rather than enabling access to a community of practice. Nearly all participants praised the personal information management functions of the devices but the teachers did not use the handhelds to access the course virtual learning environment and students did so only rarely. Email and SMS (texting) for both personal reasons and work within the school related context were more popular. Most popular were the multiple methods of recording available on the handheld: video, audio and written notes. Teachers used them to record observations on each others’ lessons, students’ work, student behaviour and trainees’ progress in teaching. Whilst the concept of blogging did not appeal and was not used by the trainee teachers, they did record personal reflections on their teaching in Word. Finally, there were clear signs that the handhelds were taken out of the participants’ pockets or bags to be used only when relevant and then replaced. This was perceived as a distinct advantage compared to desktop or even laptop based computers in the classroom with handhelds affording technology at a teacher’s side and not in their face.Graduate School of Education, University of Bristol United Kingdo

Development of ambient intelligence systems based on collaborative task models

So far, the Ambient Intelligence (AmI) paradigm has been applied to the development of a great variety of real systems. They use advanced technologies such as ubiquitous computing, natural interaction and active spaces, which become part of social environments. In the design of AmI systems, the inherent collaboration among users (with the purpose of achieving common goals) is usually represented and treated in an ad-hoc manner. However, the development of this kind of systems can take advantage of rich design models which embrace concepts in the domain of collaborative systems in order to provide the adequate support for explicit or implicit collaboration. Thereby, relevant requirements to be satisfied, such as an effective coordination of human activities by means of task scheduling, demand to dynamically manage and provide group- and context-awareness information. This paper addresses the integration of both proactive and collaborative aspects into a unique design model for the development of AmI systems; in particular, the proposal has been applied to a learning system. Furthermore, the implementation of this system is based on a blackboardbased architecture, which provides a well-defined high-level interface to the physical layer.This research is partially supported by a Spanish R&D Project TIN2004-03140, Ubiquitous Collaborative Adaptive Training (U-CAT)

Mobile Self-Management System for University Students using Mobile Application Development Lifecycle (MADLC)

This study examined the effects of self-management system in the academic enhancement for poor student who got CGPAs below 2.0 in Universiti Teknologi MARA (UiTM), Malaysia. In this research, the qualitative study was conducted to explore, describe, and examine how the self-management system helped UiTM students to develop motivation and concentration in their studies. This project used Mobile Application Development Lifecycle (MADLC) with respect to mobile applications development. The purpose of this project helped students managed themselves using the recent technologies which are mobile application, computer added learning and self-management system compared to traditional method. The proposed system showed that students that used this system improved their study and get better CGPAs

Personal Unique Time Table Generator For Students in UTP

This project is to create a new system that generate unique timetable for students in Universiti Teknologi Petronas which include function for reducing time consumption and automate student manual process during timetabling or find alternative of slot. EasyPHP is use to create dynamic web application including the algorithm. Other than that, this project also aims to improvise the current timetable in term of Human Computer Interaction where better visual design and application of colour are included. As add on, this project can provide backup timetable in various medium such as smartphone, social network and email as both of them are the most gadget and site used by students nowadays. At same time, students can do discussion from the medium aforementioned such as Facebook’s group and GoogleGroup’s thread

Automated university lecture timetable using Heuristic Approach

There are different approaches used in automating course timetabling problem in tertiary institution. This paper present a combination of genetic algorithm (GA) and simulated annealing (SA) to have a heuristic approach (HA) for solving course timetabling problem in Federal University Wukari (FUW). The heuristic approach was implemented considering the soft and hard constraints and the survival for the fittest. The period and space complexity was observed. This helps in matching the number of rooms with the number of courses. Keywords: Heuristic approach (HA), Genetic algorithm (GA), Course Timetabling, Space Complexity

REST API and Message Broker RAbbitMQ for Integration of College Academic Information System

Data integration is frequently used in a variety of applications and necessity. In the process of teaching and learning activities, STIMIK ESQ also has a Learning Management System (LMS), which is an e-learning system based on Moodle. For each class period, lecturers must complete a process to enter attendance information into the academic information system (AIS). A web-based system called AIS is used to track academic activity, class attendance, instructors, and student data. The lecturer must extract attendance information from the LMS in the form of an excel file, sort the information, and then categorize the data according to the study plan of each student. Manually entering attendance data takes between five and ten minutes. Processing all attendance for 12–15 courses takes 7–10 seconds after the assessment procedure. The STIMIK ESQ academic administrator downloads the excel file from AIS during the enrollment process and afterward creates a class on the LMS. The downloaded data is modified to fit the template specified for the LMS import procedure. Data entry for enrollment takes roughly 7 to 10 minutes. It takes between 10 and 15 seconds once the testing procedure is finished processing all courses for the odd semester of the 2020–2021 academic year. Data integration has been tested with utilizing RabbitMQ. The flexibility of REST, which can interface with two separate systems, and RabbitMQ, which can divide duties in processing a lot of data, are used in this research. In this study, the integration process is executed using a scheduler that will execute the integration process automatically

Final Year Project Intelligent Scheduling System (FISS)

Final Year Project (FYP) Intelligent Scheduling System (FISS) is an online web-based system developed to ease and assist an FYP coordinator in Universiti Teknologi Petronas (UTP) to schedule the presentations for both FYP1 and FYP2. FISS provides a one stop centre for the FYP coordinators, lecturers, examiners as well as the FYP students to enter FISS from office/home and anywhere as long as there is an internet connection. FISS proven to greatly reduce the paper usage and time saving as the FYP coordinators can use FISS to resolve any FYP schedules creation. For many years, FYP coordinators have been struggled to schedule FYP presentations due to the need to refer to various data (i.e lecturers’ data, students’ data and etc) manually. Therefore, the coordinators have to allocate extra time and if error occurs somehow they have to delay the releasing of FYP presentations’ schedule. To add more, it is difficult to identify an immediate and reliable solution to any sudden request to modify the schedule as there is no database system that stores an updated students and lecturers’ information. FISS has been developed to alleviate the problems mentioned and coordinator now can arrange the presentation schedule in a much shorter time and stored the data and information in a new database system. FISS provides an excellent interface which make the creation of a presentation’s scheduling a whole lot easier and intelligently provide solution by automatically compile various resources for the best scheduling time according to the various presentation in an FYP course

Adaptive Decision Support for Academic Course Scheduling Using Intelligent Software Agents

Academic course scheduling is a complex operation that requires the interaction between different users including instructors and course schedulers to satisfy conflicting constraints in an optimal manner. Traditionally, this problem has been addressed as a constraint satisfaction problem where the constraints are stationary over time. In this paper, we address academic course scheduling as a dynamic decision support problem using an agent-enabled adaptive decision support system. In this paper, we describe the Intelligent Agent Enabled Decision Support (IAEDS) system, which employs software agents to assist humans in making strategic decisions under dynamic and uncertain conditions. The IAEDS system has a layered architecture including different components such as a learning engine that uses historic data to improve decision-making and an intelligent applet base that provides graphical interface templates to users for frequently requested decision-making tasks. We illustrate an application of our IAEDS system where agents are used to make complex scheduling decisions in a dynamically changing environment