Embracing Technological Change in Higher Education
Open accessAbstract
Access to information has never been easier, thanks to the rapid development of the internet and communication technologies, and the ubiquity of smartphones and other internet-enabled devices. In traditional classroom learning, teachers provide students with various sources of information that are known to be reliable. Nowadays, especially in a post-pandemic era, students increasingly rely on a host of resources available on the internet. Exposure to vast amounts of scattered information could adversely affect students’ learning process. Meanwhile, pedagogical approaches, classroom learning practices, and student learning activities have evolved significantly to cope with contemporary challenges. This study reviews the current learning practices and the technological interventions in a rapidly evolving higher education landscape. In particular, the challenges when integrating technology into higher education are considered in detail and ways put forward for doing so in that context.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI and VR technologies can address physical, social, and cognitive barriers that have historically excluded students with disabilities from fully engaging in learning.
“These new emerging immersive technologies have the capability to address some physical, social, and cognitive barriers that have historically excluded students with disabilities from fully engaging and participating in the learning processes [6,37,41].”
From Impact of Artificial Intelligence and Virtual Reality on Educational Inclusion: A Systematic Review of Technologies Supporting Students with Disabilities (2024) · cites this paper as [37]
AI-driven adaptive systems can dynamically tailor learning experiences to individual needs.
“AI-driven adaptive systems can dynamically tailor learning experiences to individual needs, while VR offers immersive, multi-sensory environments that promote experiential learning.”
From Impact of Artificial Intelligence and Virtual Reality on Educational Inclusion: A Systematic Review of Technologies Supporting Students with Disabilities (2024) · cites this paper as [37]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020) · Computers & Education · doi:10.1016/j.compedu.2019.103778 · Cited by 3,585 · see citing works
EduCTX: A Blockchain-Based Higher Education Credit Platform
Turkanović, M., Hölbl, M., Košič, K., Heričko, M., & Kamišalić, A. (2018) · IEEE Access · doi:10.1109/access.2018.2789929 · Cited by 777 · see citing works
Artificial Intelligence for Student Assessment: A Systematic Review
González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021) · Applied Sciences · doi:10.3390/app11125467 · Cited by 503 · cited 1× · see citing works
“In addition, AI-based solutions, such as Expert Control System (ECS)-based tutoring platform and Agent-based Tutoring Systems (AbS) are proving to be effective in assessment for learning [19, 20].”
BackgroundBuchanan, T., Sainter, P., & Saunders, G. (2013) · Journal of Computing in Higher Education · doi:10.1007/s12528-013-9066-6 · Cited by 304 · see citing works
Internet of Things in Higher Education: A Study on Future Learning
Aldowah, H. A., Rehman, S. U., Ghazal, S., & Umar, I. N. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/892/1/012017 · Cited by 243 · see citing works
Serrano, D. R., Dea‐Ayuela, M. A., González‐Burgos, E., Gil, A. S., & Lalatsa, A. (2019) · European Journal of Education · doi:10.1111/ejed.12330 · Cited by 236 · see citing works
Baek, Y. K. (2008) · CyberPsychology & Behavior · doi:10.1089/cpb.2008.0127 · Cited by 205 · see citing works
Kurdi, B. H. A., Alshurideh, M. T., Salloum, S. A., Obeidat, Z. M., & Al-dweeri, R. M. (2020) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v14i02.11115 · Cited by 195 · see citing works
Marcinkowski, F., Kieslich, K., Starke, C., & Lünich, M. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372867 · Cited by 159 · see citing works
Opportunities and Challenges in Using AI Chatbots in Higher Education
Yang, S., & Evans, C. (2019) · International Conference on Education and E-Learning (ICEEL) · doi:10.1145/3371647.3371659 · Cited by 156 · see citing works
Use of Augmented and Virtual Reality in Remote Higher Education: A Systematic Umbrella Review
Nesenbergs, K., Abolins, V., Ormanis, J., & Mednis, A. (2020) · Education Sciences · doi:10.3390/educsci11010008 · Cited by 130 · see citing works
Critical success factors for eLearning in Saudi Arabian universities
Alhabeeb, A., & Rowley, J. (2017) · International Journal of Educational Management · doi:10.1108/ijem-01-2016-0006 · Cited by 112 · see citing works
Cruz‐Jesus, F., Oliveira, T., & Bação, F. (2018) · Journal of Global Information Management · doi:10.4018/jgim.2018040101 · Cited by 107 · see citing works
Bawaneh, A. K., Malkawi, E., & Bawa'aneh, M. S. (2020) · Contemporary Educational Technology · doi:10.30935/cedtech/8708 · Cited by 94 · see citing works
The upcoming Blockchain adoption in Higher-education: requirements and process
Harthy, K. A., Shuhaimi, F. A., & Ismaily, K. K. J. A. (2019) · MEC International Conference on Big Data and Smart City (ICBDSC) · doi:10.1109/icbdsc.2019.8645599 · Cited by 83 · see citing works
Davies, E. B., Beever, E., & Glazebrook, C. (2018) · BMC Medical Education · doi:10.1186/s12909-018-1154-x · Cited by 75 · see citing works
Challenge of effective technology integration into teaching and learning
Ramorola, M. Z. (2013) · Africa Education Review · doi:10.1080/18146627.2013.853559 · Cited by 72 · see citing works
Natural Language Processing Applications in Business
Bahja, M. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.92203 · Cited by 66 · see citing works
Self-citationUsing Machine Learning to Score Multi-Dimensional Assessments of Chemistry and Physics
Maestrales, S., Zhaı, X., Touitou, I., Baker, Q., Schneider, B., & Krajcik, J. S. (2021) · Journal of Science Education and Technology · doi:10.1007/s10956-020-09895-9 · Cited by 63 · see citing works
Shifting the Education Paradigm amid the COVID 19 Pandemic: Nursing Students’ Attitude to E Learning
Soriano, G. P., & Oducado, R. M. (2021) · Africa Journal of Nursing and Midwifery · doi:10.25159/2520-5293/8090 · Cited by 61 · see citing works
Application of Blockchain Technology in Higher Education
(2020) · European Journal of Contemporary Education · doi:10.13187/ejced.2020.3.552 · Cited by 57 · see citing works
Pikhart, M., & Klímová, B. (2020) · Societies · doi:10.3390/soc10020038 · Cited by 46 · see citing works
Shaikh, H., Khan, M. S., Maher, Z. A., Anwar, M., Raza, A., & Shah, A. (2019) · International Conference on Information Science and Communication Technology (ICISCT) · doi:10.1109/cisct.2019.8777431 · Cited by 39 · see citing works
A User-Centric Framework for Educational Chatbots Design and Development
Bahja, M., Hammad, R., & Butt, G. F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60117-1_3 · Cited by 38 · see citing works
Self-citationTowards a smart learning environment for smart city governance
Hammad, R., & Ludlow, D. (2016) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1145/2996890.3007859 · Cited by 36 · see citing works
Self-citationMoroccan High School EFL Teachers’ Attitudes and Anxiety on Using Microsoft Teams Platform
Sayeh, A. Y., & Razkane, H. (2021) · TESOL and Technology Studies · doi:10.48185/tts.v2i2.267 · Cited by 19 · see citing works
Applications of Artificial Intelligence in Assessment for Learning in Schools
Chattopadhyay, S., Shankar, S., Gangadhar, R. B., et al. (2017) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-5225-2953-8.ch010 · Cited by 16 · see citing works
Talk2Learn: A Framework for Chatbot Learning
Bahja, M., Hammad, R., & Hassouna, M. S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29736-7_44 · Cited by 14 · see citing works
Self-citationCurrent state of the application of artificial intelligence in reliability and maintenance
Pokorni, S. J. (2021) · Vojnotehnicki glasnik · doi:10.5937/vojtehg69-30434 · Cited by 14 · see citing works
Towards a generalised e-learning business process model
Hammad, R., Odeh, M., & Khan, Z. (2017) · UWE Research Repository (UWE Bristol) · Cited by 10 · see citing works
Self-citationAmkpa, S., & Abba, T. (2009) · Information Technologist (The) · doi:10.4314/ict.v6i1.48198 · Cited by 7 · see citing works
Nyagorme, P. (2018) · SSRN Electronic Journal · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Bahja M, Hammad R. A User-centric Design and Pedagogical-based Approach for Mobile Learning. The Tenth International Conference on Mobile, Hybrid, and On-line Learning. Rome, Italy: eLmL IARIA; 2018. pp. 100-105. ISBN 978-1-61208-619-4
- Hammad R. A hybrid e-learning framework: Process-based, semantically-enriched and service- oriented [doctoral dissertation]. University of the West of England; 2018. Available from: https://uwe-repository. worktribe.com/preview/874275/ HeLPS%20eLearning%20Framework. pdf [Cited: 09 October 2021]
- Washinton D.C.: Brookings Institution Press; 2009
- Naidu S, Kumar L. Factors inhibiting teachers' embracing elearning in secondary education: A literature review. Asian Journal of Distance Education. 2019;14(2):124-143
- Alhumaid K, Ali S, Waheed A, Zahid E, Habes M. COVID-19 & elearning: Perceptions &attitudes of teachers towards ELearning acceptance in the developing countries. Multicultural Education. 2020;6(2):100-116
- Authors
- Bahja, M., Amin Kuhail, M., & Hammad, R.
- Affiliations
- University of Birmingham (GB)
- University of East London (GB)
- Corresponding author
- Bahja, Mohammed
- Venue
- Higher Education - New Approaches to Accreditation, Digitalization, and Globalization in the Age of Covid
- Publisher
- IntechOpen
- Published
- 2022-07-13
- OpenAlex
- W3208727768
- Cited by
- 16
- References
- 32
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 9 · 2023: 1
- FWCI
- 6.65
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IntechOpen eBooks (publishedVersion, cc-by)
- BiblioBoard Library Catalog (Open Research Library) (submittedVersion, cc-by)
- Contact-less Assessment of In-vivo Body Signals Using Microwave Doppler Radar (InTech) (submittedVersion, cc-by-nc)
- UEL Research Repository (University of East London) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Educational Innovations and Challenges
- Physical Sciences › Computer Science › Information Systems › Educational Innovations and Technology
- Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
- Keywords
- educational technology, technology integration in higher education, student learning practices, post-pandemic learning, teaching methods, challenges of technology integration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
