10.21227/7DDH-NH55
Yusufu Yusufu Gambo
Yusufu
Yusufu Gambo
School of Computing, Engineering and Physical Sciences, University of the West of Scotland Paisley, UK
Muhammad Zeeshan Shakir
Muhammad
Zeeshan Shakir
School of Computing, Engineering and Physical Sciences, University of the West of Scotland Paisley, UK
Experimental Data for Metacognitive Skills
IEEE DataPort
2019
Artificial Intelligence
Standards Research Data
Experimental dataset
self-regulated learning process
metacognitive skills
Artificial neural network
learning agent
2019-12-05
Dataset
Creative Commons Attribution
The increasing student’s enrollment, the cost of
education and the need to make learning resources accessible
any time at anywhere are propelling educational institutions to
develop a pedagogical framework to support in-class activities
with online learning courses. Moreover, the increasing deployment
of skilled-based courses on online learning environment
occasioned by the advancement of smart and wireless technology
both in formal and informal paradigm means that there is a need
to develop effective ways to support online learning paradigm.
The transformation system brought by advancement in smart
technologies can provide a learning environment that can support
online learning process. The self-regulated learning process has
been identified as one of the effective ways of supporting
online process. The smart learning environment can be designed
and developed to support self-regulated learning process. The
metacognitive skills such as goal setting, time management, helpseeking,
task strategy and self-evaluation can be developed in
smart learning environment to provide skills enhancement to
support online learning process. Earlier, we developed artificial
neural network (ANN)-based learning agent to support the development
of smart learning environment support online learning.
However, to test our approach, there is a lack of dataset to
implement the applicability of our methodology. In this guiding
note, we present the process of generating the dataset, the training
and testing process how, and the trained weights of the dataset
can be used for predicting students’ learning style that can
trigger recommendations based on the behaviour of the student in
online learning environment. This dataset is randomly generated
simulating the response of students; there are possibilities that
using the dataset in similar purposes in future work could result
in different outcomes and this we consider a limitation of the dataset.