Home|Journals|Articles by Year Follow on Twitter

Directory for Medical Articles
 

Open Access

Research Article

JCR. 2020; 7(8): 1394-1397


PARADIGM OF SHIFT IN ASSESSMENT: CONSTRUCTIVIST AND INSTRUCTIONAL TECHNOLOGY MODEL

Rohaizan Ahmad, Rosimah Ahmad, Mohamad Irwan Sagir, Ts. Jimisiah Jaafar.

Abstract
The essence of Section 15 of Malaysian Qualifications Framework (MQF) guidelines and standards launched by Malaysian Qualifications Agency (MQA) in 2007 emphasizes eight domains of learning outcomes, which are significantly beneficial for Malaysia to ensure graduates employability. The MQF standards are in line with the Accounting and Finance related industries’ competencies requirement to meet the ever-demanding future accountants’ profession. In addition, the advancement in information and technology has prompted Institutes of Higher Learning (IHL) to utilize computer-aided or e-learning types of assessments to replace or complement the traditional paper-based methods of assessment to evaluate the achievement of the students’ skills and competencies. Educators must realize that the learning styles of students in the 21st century have moved towards student-centered learning and educators act more as facilitator in the learning process. Therefore, it is imperative for educators to evolve into more innovative types of assessments which can evaluate 21st century industries’ competencies requirement as well as a combination of active and collaborative learning tools. This study attempts to put forward a combination of Constructivist Teaching and Learning theory and technology in promoting and incorporating visual learning and assessment tools in the classrooms using a “Constructivist and Instructional Technology Model”.

Key words: graduate employability, e-learning assessment, industry competencies, constructivist, instructional technology



Similar Articles

A Transfer Learning-Based Approach with Deep CNN for COVID-19- and Pneumonia-Affected Chest X-ray Image Classification.
Chakraborty S, Paul S, Hasan KMA
SN computer science. 2022; 3(1): 17

Event-Driven Acquisition and Machine-Learning-Based Efficient Prediction of the Li-Ion Battery Capacity.
Mian Qaisar S, AbdelGawad AEE, Srinivasan K
SN computer science. 2022; 3(1): 15

Diversity Forests: Using Split Sampling to Enable Innovative Complex Split Procedures in Random Forests.
Hornung R
SN computer science. 2022; 3(1): 1

Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors.
Cova T, Vitorino C, Ferreira M, Nunes S, Rondon-Villarreal P, Pais A
Methods in molecular biology (Clifton, N.J.). 2022; 2390(): 321-347

Ultrahigh Throughput Protein-Ligand Docking with Deep Learning.
Clyde A
Methods in molecular biology (Clifton, N.J.). 2022; 2390(): 301-319


Full-text options


Latest Statistics about COVID-19
• pubstat.org


Add your Article(s) to Indexes
• citeindex.org






Covid-19 Trends and Statistics
ScopeMed.com
CiteIndex.org
CancerLine
FoodsLine
PhytoMedline
Follow ScopeMed on Twitter
Author Tools
eJPort Journal Hosting
About BiblioMed
License Information
Terms & Conditions
Privacy Policy
Contact Us

The articles in Bibliomed are open access articles licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
ScopeMed is a Database Service for Scientific Publications. Copyright © ScopeMed® Information Services.



ScopeMed Web Sites