Evidence map›Paper›PMID 38420393›Full record

ArticleHeliyon2024

The adoption and use of learning analytics tools to improve decision making in higher learning institutions: An extension of technology acceptance model.

Muaadh Mukred, Umi Asma' Mokhtar, Burkan Hawash, Hussain AlSalman, Muhammad Zohaib

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Muaadh MukredDepartment of Business Analytics, Sunway Business School, Sunway University, 5, Jalan University, Bandar Sunway, 47500, Petaling Jaya, Selangor, Malaysia.
Umi Asma' MokhtarFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
Burkan HawashFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
Hussain AlSalmanDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Muhammad ZohaibSoftware Engineering Department, Lappeenranta-Lahti University of Technology, 53851 Lappeenranta, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Learning Analytics Tools (LATs) can be used for informed decision-making regarding teaching strategies and their continuous enhancement. Therefore, LATs must be adopted in higher learning institutions, but several factors hinder its implementation, primarily due to the lack of an implementation model. Therefore, in this study, the focus is directed towards examining LATs adoption in Higher Learning Institutions (HLIs), with emphasis on the determinants of the adoption process. The study mainly aims to design a model of LAT adoption and use it in the above context to improve the institutions' decision-making and accordingly, the study adopted an extended version of Technology Acceptance Model (TAM) as the underpinning theory. Five experts validated the employed survey instrument, and 500 questionnaire copies were distributed through e-mails, from which 275 copies were retrieved from Saudi employees working at public HLIs. Data gathered was exposed to Partial Least Square-Structural Equation Modeling (PLS-SEM) for analysis and to test the proposed conceptual model. Based on the findings, the perceived usefulness of LAT plays a significant role as a determinant of its adoption. Other variables include top management support, financial support, and the government's role in LATs acceptance and adoption among HLIs. The findings also supported the contribution of LAT adoption and acceptance towards making informed decisions and highlighted the need for big data facility and cloud computing ability towards LATs usefulness. The findings have significant implications towards LATs implementation success among HLIs, providing clear insights into the factors that can enhance its adoption and acceptance. They also lay the basis for future studies in the area to validate further the effect of LATs on decision-making among HLIs institutions. Furthermore, the obtained findings are expected to serve as practical implications for policy makers and educational leaders in their objective to implement LAT using a multi-layered method that considers other aspects in addition to the perceptions of the individual user.

Indexed as

Decision-makingHigher learning institutionsLearning analyticsTechnology acceptance modelTechnology adoption

Identifiers

PMID38420393
PMCPMC10900428

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.