The COVID-19 pandemic has created cause for rapid innovation in, reimagining of, and pivoting of higher education institutions. Prior to 2020, the global higher education sector began to radically focus their efforts on creating sustainable institutions, and incorporated the United Nations’Sustainable Development Goals (SDGs). The novel coronavirus pandemic may have changed that. This systematic review examines eight manuscripts, identified through a systematic search strategy on sustainability during the COVID-19 pandemic across 2020–2021. Interestingly, the low volume of manuscripts identified highlights potential learning and teaching risks, as priorities may have shifted during rapid digitalization and emergency remote teaching practices. These manuscripts focused on Goal 4, inclusive and equitable quality education (50%);Goal 8, decent work and economic growth;Goal 9, industry, innovation, and infrastructure (37.5%);and goal enabling through integrating and embedding sustainability into the curriculum (12.5%). The implications of this systematic review highlight a need to rebuild efforts to focus on the Sustainable Development Goals, particularly considering the evolving higher education landscape during COVID-19. While there were still considerable volumes of manuscripts on higher education and sustainability during 2020–2021, the lack of contextualization to current higher education conditions should be of concern for sustainability scholars. This systematic review creates a critical foundation for accelerating our understanding of achieving SDGs in higher education during and beyond the pandemic.

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Sustainability in Higher Education during the COVID-19 Pandemic: A Systematic Review

Autism Spectrum Disorder (ASD) is a growing concern worldwide. To date there are no drugs that can treat ASD, hence the treatments that can be administered are mainly supportive in nature and aim to reduce, as much as possible, the symptoms induced by the disorder. However, diagnosis and related treatments in terms of improving communication, social and behavioural skills are very challenging due to the heterogeneity of the disorder and are amongst the largest barriers in supporting people with ASD. Thanks to the recent development in artificial intelligence (AI) and machine learning (ML) techniques, ASD can now be aimed to be detected at an early age. Also, these novel techniques can facilitate administering personalised treatments including cognitive-behavioural therapies and educational interventions. These systems aim to improve the personalised experience for the people with ASD. Acknowledging the existing challenges, this paper summarises the multitudes of ASD, the advancement of AI and ML-based methods in the detection and support of people with ASD, the progress of explainable AI and federated learning to deliver explainable and privacy-preserving systems targeting ASD. Towards the end, some open challenges are identified and listed. 

Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder

Smart Learning Objects Retrieval for E-Learning with Contextual Recommendation based on Collaborative Filtering

Group Formation (GF) strongly influences the collaborative learning process in Computer-Supported Collaborative Learning (CSCL). Various factors affect GF that include personal characteristics, social, cultural, psychological, and cognitive diversity. Although different group formation methods aim to solve the group compatibility problem, an optimal solution for dynamic group formation is still not addressed. In addition, the research lacks to supplement collaborative group formation with a collaborative platform. In this study, the next level of collaboration in CSCL and Intelligent Tutoring System (ITS) platforms is achieved. First, initial groups are formed based on students learning styles, and knowledge level, i.e. for knowledge level, an activity-based dynamic group formation technique is proposed. In this activity, swapping of students takes place on each permutation based on their knowledge level. Second, the formed heterogeneous balanced groups are used to augment the collaborative learning system. For this purpose, a hybrid framework of Intelligent Tutor Collaborative Learning (ITSCL) is used that provides a unique and real-time collaborative learning platform. Third, an experiment is conducted to evaluate the significance of the proposed study. Inferential and descriptive statistics of Paired T-Tests are applied for comprehensive analysis of recorded observations. The statistical results show that the proposed ITSCL framework positively impacts student learning and results in higher learning gains.

Dynamic Group Formation With Intelligent Tutor Collaborative Learning: A Novel Approach for Next Generation Collaboration

Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.

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WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

The new 'normal' defined during the COVID-19 pandemic has forced us to re-assess how people with special needs thrive in these unprecedented conditions, such as those with Autism Spectrum Disorder (ASD). These changing/challenging conditions have instigated us to revisit the usage of telehealth services to improve the quality of life for people with ASD. This study aims to identify mobile applications that suit the needs of such individuals. This work focuses on identifying features of a number of highly-rated mobile applications (apps) that are designed to assist people with ASD, specifically those features that use Artificial Intelligence (AI) technologies. In this study, 250 mobile apps have been retrieved using keywords such as autism, autism AI, and autistic. Among 250 apps, 46 were identified after filtering out irrelevant apps based on defined elimination criteria such as ASD common users, medical staff, and non-medically trained people interacting with people with ASD. In order to review common functionalities and features, 25 apps were downloaded and analysed based on eye tracking, facial expression analysis, use of 3D cartoons, haptic feedback, engaging interface, text-to-speech, use of Applied Behaviour Analysis therapy, Augmentative and Alternative Communication techniques, among others were also deconstructed. As a result, software developers and healthcare professionals can consider the identified features in designing future support tools for autistic people. This study hypothesises that by studying these current features, further recommendations of how existing applications for ASD people could be enhanced using AI for (1) progress tracking, (2) personalised content delivery, (3) automated reasoning, (4) image recognition, and (5) Natural Language Processing (NLP). This paper follows the PRISMA methodology, which involves a set of recommendations for reporting systematic reviews and meta-analyses.

Features of Mobile Apps for People with Autism in a Post COVID-19 Scenario: Current Status and Recommendations for Apps Using AI.

Recently Intelligent Tutoring Systems (ITS) and Computer-Supported Collaborative Learning (CSCL) have got much attention in the field of computer science, artificial intelligence, cognitive psychology, and educational technologies. An ITS is a technologically intelligent system that provides an adaptive learning paradigm for an individual learner only, while CSCL is also a technology-driven learning paradigm that supports groups of learners in pertaining knowledge by collaboration. In a multidisciplinary research field—the Learning Sciences, both individual and collaborative learning have their own significance. This research aims to extend ITS for collaborative constructivist view of learning using CSCL. Integrating both design architecture of CSCL and ITS, this research model propose a new conceptual framework underpinning “Intelligent Tutoring Supported Collaborative Learning (ITSCL)”. ITSCL extend ITS by supporting multiple learners interacting system. ITSCL support three different types of interaction levels. The first level of interaction supports individual learning by learner-tutor interaction. The second and third level of interaction support collaborative learning, by learner-learner interaction and tutor-group of collaborative learners’ interactions, respectively. To evaluate ITSCL, a prototype model was implemented to conduct few experiments. The statistical results extrapolate the learning gains, measured from Paired T-Test and frequency analysis, contend a significant learning gain and improvement in the learning process with enhanced learning performance.

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Intelligent Tutoring Supported Collaborative Learning (ITSCL): A Hybrid Framework

The concept of smart cities is the dream of every government for future development where living standards will be improved to another level. These smart cities are now being designed by fifth generation (5G) mobile communication networks. 5G technology has many characteristics that will have a positive impact on the digital experience and smart cities. In addition to uploading and downloading data faster, it also ensures very small latency and the ability to connect multiple devices at the same time. This technology can provide the reliability and the low latency required for intelligent transportation systems to smart secure parking. The current parking systems could be upgraded by introducing 5G technology. This study provides a framework that involves secure parking, finding the right parking space for different vehicle sizes, environment-friendly localization and positioning, real-time interaction voice and touch-based interaction. The proposed framework creates an optimum solution for smart secure parking and has broader implications on the application of 5G in smart cities.

Smart Parking: Novel Framework of Secure Smart Parking Solution using 5G Technology

The Hajj pilgrimage being the largest annual mass gathering globally with two to three
 million participants from over 180 counties, will remain a high priority for diseases
 surveillance for future epidemics or any other international public health emergencies
 with rapid scalability. This paper highlights the importance of monitoring mass gatherings
 during a pandemic and how mHealth applications can reduce the burden on health facilities
 during a mass gathering and tackle future infectious diseases outbreaks. The paper
 also highlights the importance of developing a user-centred application when designing
 for a diverse group of users with a shared purpose. As a result, a framework has been
 proposed to update the current applications or design and develop future mobile health
 applications. The framework has been developed based on the rationale and evidence
 found in the literature.

/pdf/a-proposed-framework-for-developing-user-centred-mobile-3av4embhrf.pdf

Aamir Anwar

Papers

Dynamic Group Formation With Intelligent Tutor Collaborative Learning: A Novel Approach for Next Generation Collaboration

Features of Mobile Apps for People with Autism in a Post COVID-19 Scenario: Current Status and Recommendations for Apps Using AI.

Intelligent Tutoring Supported Collaborative Learning (ITSCL): A Hybrid Framework

Smart Parking: Novel Framework of Secure Smart Parking Solution using 5G Technology

A proposed framework for developing user-centred mobile healthcare applications for the biggest annual mass gathering (Hajj) post COVID-19