Technology-Enhanced Learning and Teaching in COVID-19 Era: Challenges and Recommendations

Technology-enhanced learning and teaching methods have been in literature and for many years now. Many educational institutes all over the world have been using these methods to deliver their programs and degrees. Nevertheless, some institutes are not very keen on using technology in some disciplines, and deliver their programs in a traditional way for a number of reasons, especially if these have been successful and well-attended (i.e. popular) by students. In the current era, where COVID-19 pandemic has disrupted every corner of our life including higher education, technology has become a critical success factor to reduce the negative impact of this pandemic. Accordingly, it is now no longer an option to opt out from using technology in learning and teaching. This doesn’t just refer to providing (dumping) contents to students digitally, but to facilitate learning and deliver engaging and highly interactive experience to compensate for lack of face-to-face interaction between the students and their teachers and also amongst the students themselves. The use of technology in education due to COVID-19 pandemic, however, has confronted by a number of challenges. In some cases, the focus was shifted to the contents (documents, videos…etc.) rather than interactivity and student engagement. Furthermore, the students were highly overwhelmed with contents in a short period of time, which has caused anxiety, dissatisfaction and performance issues. In this paper, examples of teaching methods based on the use of technology that are employed during the lockdown period are provided. Moreover, a number of subsequent challenges due to current situation are discussed, and recommendations for implementation and best practice are shared. Also a proposal for a flipped delivery model to move forward is provided and discussed. Anecdotal student feedback has shown that the used methods and techniques were really helpful and have boosted student learning and enthusiasm in this difficult time.      </jats:p

Automatic detection, sizing and characterisation of weld defects using ultrasonic time-of-flight diffraction

Ultrasonic time-of-flight diffraction (TOFD) is known as a reliable non-destructive testing technique for weld inspection in steel structures, providing accurate aw positioning and sizing. Despite all its good features, TOFD data interpretation and reporting are still performed manually by skilled inspectors and interpretation software operators. This is a cumbersome and error-prone process, leading to inevitable delay and inconsistency. The quality of the collected TOFD data is another issue that may introduce a host of error to the overall interpretation process. Manual interpretation focuses only on the compression waves portion of the collected TOFD data and overlooks the mode-converted waves region and considers it redundant. This region may provide useful and accurate aw sizing and classification information when there is uncertainty or ambiguity due to the nature of the collected data or the type of aw, and can reduce the number of supplementary (parallel) B-scans by utilising the (longitudinal) D-scans only. The automation of data processing in TOFD is required to minimise time and error and towards building a comprehensive computer-aided TOFD interpretation tool that can aid human operators. This project aims at proposing interpretation algorithms to size and characterise flaws automatically and accurately using data acquired from D-scans only. In order to achieve this, a number of novel data manipulation and processing techniques have been specifically developed and adapted to expose the information in the mode-converted waves region. In addition, several multi-resolution approaches employing the Wavelet transform and texture analysis have been used in aw detection and for de-noising and enhancing quality of the collected data. Performance of the developed algorithms and the results of their application have been promising in terms of speed, accuracy and consistency when compared to human interpretation by an expert operator, using the compression waves portion of the acquired data. This is expected to revolutionise the TOFD data interpretation and be in favour of a real-time processing of large volumes of data. It is highly anticipated that the research findings of this project will increase significantly the reliance on D-scans to obtain high sizing accuracy without the need to perform further B-scans. The overall inspection and interpretation time and cost will therefore be reduced significantly

Hybrid Learning Using Canvas LMS

Hybrid learning refers to the learning style where online components are used to replace some face-to-face elements of the course. In the current era, where COVID-19 pandemic has highly impacted higher education, online and remote forms of learning have become critical success factors to deliver engaging and rich teaching and learning experience to the students. With the partial return of face-to-face interaction with the students this year after easing restrictions, universities have no choice but to offer hybrid learning experience. In the journey towards this type of learning, a transition in both pedagogy and vehicle (tools) is inevitable. Hybrid learning pedagogy has been in literature for many years now and many institutions worldwide have enough experience to run courses and programs as a hybrid model. For the vehicle, a number of tools are necessary to facilitate delivery, but the most important tool is obviously the learning management system (LMS). Canvas LMS is now considered one of the most commonly used electronic learning systems, offering a large number of features and options to make teaching and learning easier and effective for both teachers and students. In this paper, two hybrid learning models are proposed. An example of implementing one of the two models using Canvas LMS and other supporting tools is provided. Anecdotal student feedback has shown that the students were highly engaged and their experience has been improved as a result of the hybrid delivery format.</jats:p

Technology-Enhanced and Personalised Laboratory Learning Experience for Undergraduate Electrical Engineering and Electronics Students

Teaching large and multicultural cohorts in lecture theatres is often a challenging task, and it becomes more challenging when it comes to laboratory teaching where students carry out practical work is involved. Students often complain about the quality of delivery with regards to the support they get from teaching assistants and technicians, and the lack of meaningful and personalised feedback they receive afterwards. Taking into consideration the fact that cohort sizes are often caused to increase from year to year, a serious sustainability issue therefore arises. Students in such cohorts may eventually disengage from their studies as a result of their perception of a lack of personalised learning experience. This often combines with other compounding factors into a downward spiral of disillusionment and demotivation that further jeopardises their studies and makes subsequent re-engagement less likely. Furthermore, the physical capacity restrictions of the laboratories and resources impose a further limit on how work is organised, and with constant budget cuts and increasing expectations and workloads, some form of crisis may seem inevitable.&#x0D; This paper portrays how such a crisis was averted by implementing a package of transformational change delivered in a planned, incremental fashion over a period of 5 years to bring a notable improvement in the overall laboratory and practical coursework provision to second year Electrical Engineering and Electronics students, by employing a number of innovative approaches to enhance student experience. Moreover, the incorporation of tools such as instructional videos, online pre-lab and post-lab questions, blogs for student projects, weekly FAQs and Twitter feeds were particularly innovative and effective in their deployment, and resulted in a win-win situation in which both students and staff were able to communicate instantly and asynchronously in a manner that was hitherto not possible. This is particularly timely as the continuous increase in student numbers means that such techniques will be used increasingly. As a result, student satisfaction has improved in a steady and quantifiable manner, with a 29% increase over three academic years.</jats:p