Challenges and Opportunities in the Internet of Intelligence of Things in Higher Education—Towards Bridging Theory and Practice
Abstract
:1. Introduction
- RQ1: What is the current state-of-the-art in relation to the integration of intelligence in the IoT for education?
- RQ2: What are the principal components of an IoIT architecture for education?
- RQ3: To what extent are practitioners ready to implement IoIT in their teaching and learning activities?
- RQ4: What are the various elements of intelligent teaching and learning and how can they be applied?
2. Literature Review (RQ1)
2.1. Systematic Literature Review
2.2. Bibliometric Analysis
1 | TS = (internet of things) | 4574 |
2 | AB = (internet of things) | 3292 |
3 | TI = (internet of things) | 954 |
4 | (TS = (internet of things) AND AB = (artificial intelligence)) | 487 |
5 | ((TS = (internet of things)) AND AB = (artificial intelligence)) AND AB = (education) | 42 |
6 | TI = (internet of things) | 964 |
7 | (TI = (internet of things)) AND TI = (artificial intelligence) | 24 |
8 | ((TI = (internet of things)) AND TI = (artificial intelligence)) AND TS = (education) | 0 |
9 | (TS = (education)) AND TI = (internet of things) | 47 |
10 | ((TS = (education)) AND TI = (internet of things)) AND ALL = (artificial intelligence) | 2 |
11 | ((TS = (education)) AND AB = (internet of things)) AND ALL = (artificial intelligence) | 41 |
12 | (TS = (internet of things)) AND AB = (education) | 256 |
2.2.1. The Internet of Things
2.2.2. The Internet of Intelligence of Things
2.2.3. The Internet of Intelligence of Things in Education
3. Development of Conceptual Model (RQ2)
3.1. Building Blocks of IoITE
3.2. Contextual View of IoIT in Education
4. Exploratory Pilot Survey Study (RQ3)
4.1. Methodology
4.2. Results and Analysis
5. Application of IoIT in Education (RQ4)
5.1. Use Case Scenario 1: Education:
5.1.1. Use Case 1—Detailed: Classroom Management
- place students in a classroom strategically,
- consider students’ learning abilities (or disabilities that will affect the requirements of assignments and exams),
- monitor students’ anxiety level via smartwatches,
- ping students in real time on whether they understand the lecture and if not, ask questions and connect to the professor’s device to mitigate the progression of learning,
- propose a schedule for students’ workload on the different assignments to meet deadlines and prepare for exams and
- match compatible students into special groups, such as study buddies and collaborative project work.
- Possibilities
- [Communication Mode] Live for face-to-face interaction.
- [Interaction Types (Traditional)] Learner–Teacher, Learner–Content, Teacher–Content.
- [Interaction Types (IoITE)] Learner–Sensory-of-Things, Learner–Effectors-of-Things, Learner–AI-Environment, Teacher–Effectors-of-Things.
- [Interaction Context] N/A.
- [Content Type] Active.
- [Content Creation] Socialisation and internationalization.
- [Content Form] Tacit.
- [SoT] Attendance and emotional recognition.
- [EoT] Proximity, Emotional and learning states.
- [eTools (IT-Mediated)] Peer-to-peer collaboration and competition.
- [eTools (Intelligent)] Simulation, competition, automated assessment, and gaming.
- [Levels of Intelligence (~data)] Collection.
- [Levels of Intelligence (~System)] Platform.
- [Levels of Intelligence (~Network)] Local.
- [Pedagogy] Cognitivism, behaviorism, and constructivism.
- [Teaching and Learning Forms] Coaching, Mentoring.
- Requirements…
- Design/configuration
- Learning activities
5.1.2. Use Case 2—Brief Example: Proctoring Exam
5.2. Use Case Scenario 2: Learning
5.2.1. Use Case 3—Detailed: Game-Based Learning—Engagement
- Possibilities
- [Communication Mode] Synchronous (Synch).
- [Interaction Types (Traditional)] Learner–Teacher, Learner–Content.
- [Interaction Types (IoITE)] Learner–Sensory-of-Things, Learner–Effectors-of-Things, Learner–AI-Environment, Teacher–Effectors-of-Things.
- [Interaction Context] N/A. May be inter- or intra-INST depending on the game configuration.
- [Content Type] Active.
- [Content Creation] Socialization and internalization.
- [Content Form] Tacit.
- [SoT] Emotional recognition and attendance.
- [EoT] EMO; Learning state.
- [eTools (IT-Mediated)] Chat, Collaboration, Multimedia, Competition.
- [eTools (Intelligent)] Simulation, Automation, Gaming, Experience-Oriented.
- [Levels of Intelligence (~data)] Decision-making.
- [Levels of Intelligence (~System)] Embedded.
- [Levels of Intelligence (~Network)] N/A.
- [Pedagogy] Constructivism, Self-Directed.
- [Teaching and Learning Forms] Coaching, Mentoring, Experiential.
- Requirements
- Design/configuration
- Learning activities
- Study market conditions and performance goals and develop a one-year strategy.
- Group dynamics, roles, and responsibilities. Establish communications standards.
- Identify variables from the SAP system to capture and enter a decision support algorithm using Microsoft Excel.
- Run company for one quarter.
- The professor displays results and ranks the companies’ performance, followed by analysis, reflection, and discussion.
- Reassess strategy and decision-making algorithm.
- Run two more quarters on the same cycle.
- Produce a comprehensive annual report including analytics, comparative performance interpretation, and lessons learned.
- Complete a survey on the state of flow and group dynamics.
5.2.2. Use Case 4—Brief Summary: AI-Assisted Structured Online Group Discussion
6. Challenges and Discussion
6.1. Realizing the IoT
- The diversity, scale, and complexity of different technologies need to be interconnected in an intelligent way via sensors, such as cameras, biometrics, physical, and chemical, which also need to be nonintrusive, transparent, and invisible. These technologies need to resolve issues of compatibility, deployment, cost-benefit, dependencies, and management thereof, which entail some of the more important barriers to the application of IoT by different stakeholders.
- Investment and adoption of necessary and appropriate hardware with embedded intelligence for the smart management of power usage, bandwidth, various systems, and services.
- Privacy and security have been and remain at the top of the agenda for the IoT and all its applications. Important challenges include the adaptability and suitability of security architecture to different applications. ‘One model fits all’ is not efficient and may even lack effectiveness.
- In the e-commerce arena, contrary to mature applications, the IoT is riddled with possibilities (see Table 6 in the context of education), uncertainties, and inequities. This makes business models much more complex to devise and subsequently, makes technical requirements more challenging to specify and implement.
- Sustainability of IoT, although feasible for small applications, is much more difficult to manage and much more costly for the larger enterprise. IoT must be part of the organization’s digital strategy plan where the traditional IT department would need to be redesigned into the IoT department. Some barriers, such as traditional outsourcing models, need to be reconsidered and their business model redesigned to achieve IoT agility within the organization.
6.2. Barriers to IoIT in Education
- Academic institutions usually have an IT department to deal with software and hardware infrastructures. The department is far removed from the business of education, and, therefore, their support for teachers and students does not exist when it comes to using technology for teaching and learning. This is still true even though many academic institutions establish instructional technology units to bridge this gap. Unfortunately, instructional technology specialists lack depth of knowledge in IT and, more specifically, in IoT. This challenge can only be addressed via continuous IoT professional development training at the edge of teaching and learning, namely for professors and teaching assistants.
- Academic administration tends to regulate the use of IT (and, by extension, IoT) and its use in the learning environment, thereby limiting any type of teaching and learning innovation. This begs the question of the extent of academic freedom in the attempt to innovate in teaching, learning, and education research. Academic freedom to the extent of IoT use has not been addressed. Fear of repercussions remains and, as such, IoT application is suppressed.
- The connection via devices between behavioral, physiological [31,55,56], and administrative functions is a major challenge for higher education academic institutions. This is primarily due to the lack of understanding of the benefits, which include management of people flow, classroom utilization, energy, attendance, physical and psychological wellness, registration, and learning characteristics, such as attention.
6.3. Emerging Artificial Intelligence and Promise for the Education Landscape
6.4. Digital Transformation Capabilities and Limitations
6.5. The New Paradigm of Learning Analytics
7. Conclusions
- The need for institutions to have an integrated educational intelligence architecture (Figure 3) for their administrative and management functions (including classroom management). This implies the commitment and effective alignment of digital structures, data strategy, learning strategy, and digital platforms and services into the overarching institutional digital strategy.
- Consequently, the operationalizing of teaching and learning intelligence mechanisms can be realized via the configuration of the IoT and AI in the learning process by professors. With the alignment (Figure 3), this configuration can take place at the data, information, knowledge, and intelligence layers of the learning process. Therefore, every layer would have targeted intelligence serving the learning process, from comfortable learning spaces to innovative and customized/personalized pedagogies.
- However, today, our exploratory survey shows that, in general, professors in higher education have limited use of IoIT in their own personal lives, and only a few have used it in their practice of teaching and learning. These findings have important implications for institutions, such that they need to have sustainable mechanisms for the continuous professional development of educators with regard to IoT and AI, as well as policies that encourage them to use IoT and AI tools via financial and service support while advising and protecting them from potential backlash and other serious or benign liabilities. Today’s institutional strategies do not seem to take People-of-Things into consideration.
- From a learning integration perspective, it would be logical to say that for professors to have the capabilities to use IoT in their practice, then, they would need to have a good knowledge of the IoT and AI concepts. Our exploratory study shows that around two-thirds of the professors reported to have little or no knowledge of sensors and effectors, which are the most critical element in the implementation of IoT in any context. Our study results, therefore, advocate for education regarding IoT sensors and effectors to all professors and potential uses in their practice.
- The top five uses of information technologies that were reported by surveyed professors include learning management systems, messaging, streaming videos, video conferencing, digital books, and smartphones, all of which are not necessarily IoT tools but simply indicative of the extent at which professors have used digital tools in their classroom. When asked about their use of smart apps or sensors/effectors of things, such as smart apps, artificial intelligence tools for learning, smart tutoring, and gaming, only a few reported having used them. Our study results, therefore, advocate for the need to facilitate the support of sensors and effectors in an attempt to encourage their use by professors in their practice.
- Overall, institutions
- can use our conceptual model of IoITE in the development of their digital strategy, assess the extent of their capabilities to currently use IoIT in their learning processes, and develop a strategy for its implementation aligned with its strategic directions, and
- can reproduce the survey to assess the level of readiness of their professors in using IoIT in their classrooms for teaching and learning. The results can help management in establishing action plans to meet the institution’s strategic planning.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
Abbreviations | Description |
ABS | Abstract Search |
AIoT | Artificial Intelligence of Things |
AI | Artificial Intelligence |
ALL | Search in all fields |
AmI | Ambient Intelligence |
ASYNC | Asynchronous |
BD | Big Data |
DT | Digital Transformation |
EOST | Experience-Oriented Smart Things |
EoT | Effectors of Things |
ER | Emotional Recognition |
ERP | Enterprise Resources Planning |
EXPL | Experiential |
INST | Institutional |
IoA | Internet of Agents |
IoD | Internet of Devices |
IoT | Internet of Things |
IoIT | Internet of Intelligence of Things |
IoITE | Internet of Intelligence of Things in Education |
IoPF | Internet of Platforms |
IoP | Internet of People |
LA | Learning Analytics |
ML | Machine Learning |
SoT | Sensors of Things |
SO | Service-Oriented |
TS | Topic Search |
TRANS | Transmission |
WoS | Web of Science |
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Steps | Description | Purpose |
---|---|---|
Concept Coding | Broad search | Identification of key terms |
Scoping | Identifying the boundaries of key terms | Delimiting keywords |
Concept Search Plan | Developing a strategy for searching | Specifying search steps |
Inclusion/Exclusion | Establishing and applying a set of criteria to apply search results | Filtering search results |
Refinement | Reading articles in full and further consideration to retain or not | Selecting final set of articles |
Analysis | Processing retained set of articles for insight and building on the work of previous researchers | Bibliometric analysis |
Article | Areas | Digital Innovations |
---|---|---|
| Education | Wearable computing |
| Management on campus | Information system |
| Professor perceptions | N/A |
| Social learning theory | Intelligence |
| Education; Systematic literature review | N/A |
| Business process (academia) | Blockchain |
| Education | AI |
| Education; Smart Campus; Classroom | IoT and AI |
| Learning | Digital platforms |
| Education; Decision-making | N/A |
| Economy; Education | BD; AI; ML |
| International economy; Education | Blockchain; AI |
| Education; Economy | BD; A; ML |
| Computer science; Engr; Education | Voice assistant; AI |
| Business Model; Education | N/A |
| Education | N/A |
| Higher education | N/A |
| Architecture; Academia; Software engineering | AI; Distributed computing |
In the following questions, “education” implies all university functions (administrative, facility, management, classrooms, laboratories, computers) and learning processes (in class), and “IoT” refers to the Internet of Things in terms of devices and applications, including intelligence, and the use of mobile devices, laptops, wearables, smart classes, smart campus. Examples:
| |
In the following questions, we implicitly refer to IoT as IoT devices, applications, and intelligence. Therefore, whenever the word IoT appears, it refers to IoT devices, applications, and intelligence, as elaborated in the list above. | |
How much do you feel you understand the following concepts? [Scale: Not at all, Not Well, Neutral, Well, Very well] | |
IoT devices | Cloud computing |
IoT applications | Cryptocurrency |
Data science and& analytics | Sensors and& actuators |
Artificial Intelligence | Blockchain |
Digital innovation and transformation | |
Which of the following IT TOOLS have you used in education and/or for learning? [Scale: Always, Very Often, Sometimes, Rarely, Never] | |
Learning management systems | Online government portal |
Messaging from your institution | Digital whiteboard |
Streaming videos | Electronic readers |
Video conferencing/meeting | Artificial intelligence tools |
Digital books | Animations |
Smart phone | Augmented reality |
Wearables such as smart watch | Massive Multiplayer Online Role-Playing Game |
(MMORPG) | |
Text messaging | Attendance tracking |
COVID-19 Tracker app | Online peer- to- peer learning |
Notebook/iPad | Smart apps~geospatial tracking |
Simulations | Smart tutoring systems |
Smart facility controls | Cryptocurrency |
Physical space access controls |
IoT Concepts | Not at All | Not Well | Neutral | Well | Very Well | Average * |
---|---|---|---|---|---|---|
IoT devices | 0 | 8 | 8 | 60 | 24 | 4.00 |
IoT applications | 0 | 8 | 12 | 60 | 20 | 3.92 |
Data science and analytics | 4 | 8 | 16 | 40 | 32 | 3.88 |
Artificial Intelligence | 4 | 16 | 20 | 40 | 20 | 3.56 |
Digital innovation and transformation | 4 | 12 | 24 | 44 | 16 | 3.56 |
Cloud computing | 4 | 28 | 8 | 28 | 32 | 3.56 |
Cryptocurrency | 8 | 28 | 12 | 40 | 12 | 3.20 |
Sensors and actuators | 12 | 24 | 24 | 24 | 16 | 3.08 |
Blockchain | 20 | 16 | 20 | 32 | 12 | 3.00 |
IoT Tools | Always (%) | Very Often (%) | Sometimes (%) | Rarely (%) | Never (%) |
---|---|---|---|---|---|
Learning management systems | 43 | 18 | 25 | 0 | 12 |
Messaging from your institution | 35 | 35 | 14 | 7 | 7 |
Streaming videos | 25 | 37 | 18 | 18 | 0 |
Video conferencing/meeting | 22 | 55 | 11 | 5 | 5 |
Digital books | 20 | 40 | 13 | 6 | 20 |
Smartphone | 10 | 40 | 25 | 25 | 0 |
Wearables such as smartwatch | 12 | 6 | 0 | 12 | 68 |
Text messaging | 14 | 28 | 14 | 28 | 14 |
COVID-19 Tracker app | 12 | 12 | 0 | 6 | 68 |
Notebook/iPad | 6 | 20 | 40 | 20 | 13 |
Simulations | 7 | 23 | 38 | 15 | 15 |
Smart facility controls | 8 | 8 | 16 | 8 | 58 |
Physical space access controls | 8 | 16 | 16 | 8 | 50 |
Online government portal | 7 | 7 | 7 | 0 | 76 |
Digital whiteboard | 0 | 21 | 14 | 28 | 35 |
Electronic readers | 0 | 7 | 7 | 7 | 78 |
Artificial intelligence tools | 0 | 8 | 25 | 25 | 41 |
Animations | 0 | 21 | 21 | 28 | 28 |
Augmented reality | 0 | 7 | 23 | 7 | 61 |
MMORPG | 0 | 7 | 7 | 0 | 84 |
Attendance tracking | 0 | 33 | 25 | 8 | 33 |
Online peer-to-peer learning | 0 | 42 | 19 | 28 | 14 |
Smart apps~geospatial tracking | 0 | 15 | 7 | 23 | 53 |
Smart tutoring systems | 0 | 23 | 23 | 30 | 23 |
Cryptocurrency | 0 | 15 | 7 | 7 | 69 |
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Dimensions | Factors/Determinants | ||
---|---|---|---|
| Synch | Asynch | live |
Real-time | Batch | ||
| Learner–Learner | Learner–Teacher | Learner–Content |
Teacher–Teacher | Teacher–Content | Teacher–TA | |
Learner–TA | TA–Content | ||
| Learner–Sensors-of-Things | L–Effectors-of-Things | Teacher–Sensor-of-Things |
Teacher–Effectors-of-Things | TA–Sensors-of-Things | TA–Effectors-of-Things | |
Content–Sensors-of-Things | Content–Effectors-of-Things | Content–AI-Teachers | |
Content–AI-Content | Content–AI-TA | Content–AI-Learner | |
Learner–AI-Environment | |||
| Inter-INST | Intra-INST | Regional |
National | INTL | ||
| Passive | Active | Adaptive |
| Socialization | Externalization | Internalization |
Combination | |||
| Tacit | Explicit | |
| Alerts | Announcements | Due dates |
Proximity | Human flow | Attendance | |
Location | Emotional Recognition | ||
| Tracking | Proximity | Emotional State |
Health | Learning state | ||
| Chat | Forum | Wiki |
Messaging | Blogs | Peer2Peer | |
Collaboration | Multimedia | Competition | |
| Adaptive | Recommendation | Automation |
Simulation | Customization | Agents | |
Competition | Ambient | Distributed | |
Auto Assessment | Planning | Gaming | |
Experience Oriented | |||
| Collection | Transmission | Treatment |
State of notification | Decision-making | ||
| Distributed | Platform | SO |
Embedded | Context-Aware | ||
| Local | Global | Vertical |
Horizontal | |||
| Behaviorism | Cognitivism | Constructivism |
Connectivism | Self-Directed | Hybrid | |
| Tutoring | Coaching | Mentoring |
Direct | Cooperative | Experiential |
IoIT of | Networks | Systems | Devices | Platforms | People | |
---|---|---|---|---|---|---|
1. | Communication Mode | ✓ | ||||
2. | Interaction Types (Traditional) | ✓ | ||||
3. | Interaction Types (IoITE) | ✓ | ✓ | |||
4. | Interaction Context | ✓ | ✓ | |||
5. | Content type | ✓ | ✓ | |||
6. | Content creation | ✓ | ✓ | |||
7. | Content form | ✓ | ✓ | |||
8. | SoT | ✓ | ||||
9. | EoT | ✓ | ||||
10. | eTools (IT-mediated) | ✓ | ✓ | ✓ | ||
11. | eTools (Intelligent) | ✓ | ✓ | ✓ | ||
12. | Levels of Intelligence (~data) | ✓ | ✓ | |||
13. | Levels of Intelligence (~System) | ✓ | ✓ | |||
14. | Level of Intelligence(~Network) | ✓ | ✓ | |||
15. | Pedagogies | ✓ | ✓ | |||
16. | Teaching and learning forms | ✓ | ✓ |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Saadé, R.G.; Zhang, J.; Wang, X.; Liu, H.; Guan, H. Challenges and Opportunities in the Internet of Intelligence of Things in Higher Education—Towards Bridging Theory and Practice. IoT 2023, 4, 430-465. https://doi.org/10.3390/iot4030019
Saadé RG, Zhang J, Wang X, Liu H, Guan H. Challenges and Opportunities in the Internet of Intelligence of Things in Higher Education—Towards Bridging Theory and Practice. IoT. 2023; 4(3):430-465. https://doi.org/10.3390/iot4030019
Chicago/Turabian StyleSaadé, Raafat George, Jun Zhang, Xiaoyong Wang, Hao Liu, and Hong Guan. 2023. "Challenges and Opportunities in the Internet of Intelligence of Things in Higher Education—Towards Bridging Theory and Practice" IoT 4, no. 3: 430-465. https://doi.org/10.3390/iot4030019