Work in Progress - Laboratory Learning System for Simultaneous Multi-point Environmental Factors Measurement

The existence of a touch of technology on laboratory learning becomes another alternative as a supporter of laboratory learning. Different practitioner's wishes and intensity of relatively short laboratory practice which resulted in dissatisfaction in the implementation of a practicum. Thus, an intelligent learning alternative is needed. This intelligent learning aims to provide high-quality and high-performance training skills that can assist the practitioner in solving problems related to practicum materials. The intelligent learning system is a learning system that handles some student instruction without any intervention from a teacher.Alternative learning system that can support the creation of Intelligent Learning System is by Natural Language Processing (NLP) method. This final project provides an explanation of the creation and implementation of intelligent learning systems in the Object Oriented Programming Computer Laboratory. This system consists of several stages: parsing, similarity, stemming, Knowledge Base which is designed in an interactive form between praktikan and agent based dialoge based application. The success rate of this system in answering questions from praktikan session II is 88.75%.

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An Open-Ended Question Self-Explanation Classification Methodology for a Virtual Laboratory Learning System

2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI) ◽

10.1109/iiai-aai.2018.00052 ◽

2018 ◽

Author(s):

Qi-Zhone Huang ◽

Chih-Chao Hsu ◽

Tzone I Wang

Keyword(s):

Learning System ◽

Virtual Laboratory ◽

Laboratory Learning

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Measuring E-Learning System Adoption in Universities in Tanzania

International Journal of ICT Research in Africa and the Middle East ◽

10.4018/ijictrame.2020070101 ◽

2020 ◽

Vol 9 (2) ◽

pp. 1-18

Author(s):

Deogratius Mathew Lashayo

Keyword(s):

Environmental Factors ◽

Structural Equation ◽

Learning Systems ◽

Learning System ◽

Clear Understanding ◽

Perceived Benefits ◽

Is Success ◽

Actual Use ◽

E Learning ◽

The University

The success of e-learning systems in Tanzania relies on various factors that influence its measurement. Examples of the key factors include trust, environmental factors, and the university readiness. However, influence of these factors towards e-learning systems is not clear. Understanding their impacts and significance helps decision makers and stakeholders in making informed decisions on how to handle them. This study modifies the information systems (IS) success model whereby it adopts 12 factors that had been suggested by this author in his previous study conducted in Open University of Tanzania (OUT) in 2017. A sample of 1,005 students from eight universities in Tanzania was collected. A structural equation modelling was used in data analysis. The results shows trust (T) has positive and significant impact on e-learning actual use (EAU) while environmental factors (EF) had positive and significant impacts on e-learning actual use and perceived benefits, and at the same time, university readiness had a positive and significant impact on perceived benefits (PB).

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