Technology Integration Models and Frameworks in Teaching and Training

The purpose of this study is to review the existing models and frameworks which has been implemented for technology integration during teaching and training. As discussed in the numerous literature Technological Pedagogical and Content Knowledge (TPACK), Substitution, Augmentation, Modification, Redefinition (SMAR), TPACK-based ID models such as TPACK-Comprehension, Observation of instruction, Practice of instruction, and Reflection on TPACK (TPACK-COPR) model, Introduce-TPACK, Demonstrate, Develop, Implement, Revise - a TPACK-based lesson, and Reflect on a TPACK-based lesson (TPACK-IDDIRR1) model, and TPACK-IDDIRR2 model have been applied by today’s instructors and trainers to achieve the specific goal for effective teaching and training. This paper intends to highlight the key features of the above-mentioned models and frameworks with few hurdles as found in the empirical-based studies. It also discusses how those hurdles could be mitigated by addressing the extraneous cognitive load of instructors as well as trainers to carry out technology integration with future recommendations for the research. It was found that specific frameworks and models are limited to the macro-level concept but today’s instructors, as well as trainers, are required to have adequate instructional guidance in chronological steps so that they could implement those models and frameworks in their teaching and training for productive outcomes.

Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis

A learner’s cognitive load is highly associated with their academic achievement within learning systems. Diagnostic information about a learner’s cognitive load is useful for achieving optimal learning, by enabling the learner to manage and control their cognitive load in the e-learning environment. However, little empirical research has been conducted to obtain diagnostic information about the cognitive load in e-learning systems. The purpose of this study was to analyze a personalized diagnostic evaluation for a learner’s cognitive load in an e-learning system, using the Bayesian Network (BN) as a learning analytic method. Data from 700 learners were collected from Cyber University. A learner’s cognitive load level was measured in terms of three components: extraneous cognitive load, intrinsic cognitive load, and germane cognitive load. The BN was built by representing the relationship among the extraneous cognitive load, intrinsic cognitive load, germane cognitive load, and academic achievement. The conditional and marginal probabilities in the BN were estimated. This study found that the BN provided diagnostic information about a learner’s level of cognitive load in the e-learning system. In addition, the BN predicted the learner’s academic achievement in terms of their different cognitive load patterns. This study’s results imply that diagnostic information related to cognitive load helps learners to improve academic achievement by managing and controlling their cognitive loads in the e-learning environment. In addition, instructional designers are able to offer more appropriately customized instructional methods by considering learners’ cognitive loads in online learning.

Understanding Cognitive Load in Digital and Online Learning: a New Perspective on Extraneous Cognitive Load

Cognitive load theory has been a major influence for the field of educational psychology. One of the main guidelines of the theory is that extraneous cognitive load should be reduced to leave sufficient cognitive resources for the actual learning to take place. In recent years, research regarding various design factors, in particular from the field of digital and online learning, have challenged this assumption. Interactive learning media, immersion, disfluency, realism, and redundant elements constitute five major challenges, since these design factors have been shown to induce task-irrelevant cognitive load, i.e., extraneous load, while still promoting motivation and learning. However, currently there is no unified approach to integrate such effects into cognitive load theory. By including aspects of constructive alignment, an approach aimed at fostering deep forms of learning in order to achieve specific learning outcomes, we devise a strategy to balance cognitive load in digital learning. Most importantly, we suggest considering both the positive and negative effects on cognitive load that certain design factors of digital learning can cause. In addition, a number of research results highlight that some types of positive effects of digital learning can only be detected using a suitable assessment method. This strategy of aligning cognitive load with desired learning outcomes will be useful for formulating theory-guided and empirically testable hypotheses, but can be particularly helpful for practitioners to embrace emerging technologies while minimizing potential extraneous drawbacks.

Managing LearningThrough Mathematical Literacy Based on Cognitive Load

The object of mind is something that must be taught by the teacher to the student in the form of learning, or we know that the teaching is the process ofinteraction between students, between students and teachers, and learning resources in the learning environment so that with it we as educators need to optimize thestudent's thought process so that students can be optimal for information. There are three sources of how students obtain optimization in the learning process, namely: intrinsic cognitive load (dependingon the difficulty level of amaterial), extraneous cognitive load (depending on the presentation of thematerial) and germane cognitive load (whichis imposed by teaching methods that lead to better learning outcomes). the results showed that the interactive element is well managed and extraneous cognitive load is suppressed to a mable so that it creates a large enough germane cognitive load.

Collaborative note-taking affects cognitive load: the interplay of completeness and interaction

Studies showing improved learning performances for students who take notes collaboratively have speculated that sharing this task among group members may reduce the extraneous cognitive burden placed on each member. Therefore, a study (n = 171) was conducted in the context of a flipped scientific writing course to examine the effects of collaborative note-taking on student’s levels of cognitive load. Students in the course were divided into two groups, with members of the treatment group being directed to take collaborative notes in a shared online document and members of the control group receiving no such instructions. The study also measured the level of collaboration the collaborative note-takers engaged in, as well as the level of completeness of the notes that they produced. The results showed that, firstly, the treatment group reported higher levels of both germane and extraneous cognitive load compared to those of the control group, meaning that collaborative note-takers experienced higher levels of understanding of course content as well as increased confusion. Secondly, the level of collaboration was positively and significantly correlated with levels of germane load (understanding), but not with extraneous load (confusion). Thirdly, no correlation was found between completeness of notes and cognitive load. Accordingly, the authors suggest that collaborative note-taking is worthwhile, as the gains to students’ understanding of course content outweigh the disadvantages of increased confusion.

Students’ extraneous cognitive load in cell biology lectures

This study aims to analyse students’ extraneous cognitive load (ECL) in cell biology lectures. Participants in the study were 31 students of the Biology Education Department who attended the Cell Biology course from a university in Jakarta, Indonesia. The Cell Biology lectures include fours topics. The data of ECL were measured using questionnaires with a semantically differential scale, containing statements about students’ mental efforts in understanding the information received in the lectures. The data obtained were then tabulated, categorised according to the mental effort rubric, and made into percentage for each step of the VARK (Visual, Aural, Read/write, Kinaesthetic) approach. The results of the data analysis show that students' mental effort (ECL) in understanding each concept in Cell Biology lectures through the VARK approach is generally in the lower category. This is indicated by the very high percentage in the low category for visual, aural, read/write, and kinaesthetic steps. Keywords: Extraneous cognitive load, cell biology, VARK;