A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</jats:p

Virtual student-led neuroscience conferencing: a UK multicentre prospective study investigating delegate outcomes and delivery mode

Background Clinical neuroscience training programmes are becoming increasingly competitive to enter. UK university neuroscience societies act as a local environment for students to develop their career interests and provide portfolio building opportunities through hosting events such as annual conferences. Recently there has been a transition to more of these events being held online yet the impact of this, if any, remains unclear. This prospective study aimed to identify the impact of student-led neuroscience conferences on delegates and examine attitudes towards an online delivery approach. Methods Multi-centre prospective survey study using pre-conference, post-conference, and 6-month post-conference online questionnaires distributed at 6 virtual student-led neuroscience conferences in 2021. The questionnaires had five-domains: demographics, career aspirations, academic skillsets, an educational manipulation check (EMC) and mode of delivery preference. Results Nine hundred twenty-four surveys were completed across 559 conference attendances. 79.9% of delegates were medical students. Interest in a neuroscience career (p < 0.001), preparedness to undertake research (p < 0.001) and presentation (p < 0.001), as well as EMC scores (p < 0.001) increased immediately post conference. Most participants at 6 months post-attendance had completed an academic project (71.9%) or presentation (50.9%), although 88.8% were lost to follow up. Online format was preferred (65%) with reasons including elimination of travel and access to home facilities whilst lack of face-to-face interaction and engagement were recognised limitations. Conclusion UK student-led online neuroscience conferences play a role in developing knowledge and may facilitate career interest, academic skillset and longer term portfolio building. A hybrid virtual and in-person experience would offer an ideal solution to future conferencing, providing options promoting engagement and interactivity whilst advocating sustainability, accessibility and widening participation

Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements

Background: Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value. Methods: A deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018). Results: All deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 ± 3.5 cm2, 91.2 ± 4.5 cm2 and 93.2 ± 3.2 cm2, respectively. Minimal RA area mean and SD DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 was 89.8 ± 3.9 cm2, 87.0 ± 5.8 cm2 and 91.8 ± 4.8 cm2. Automatic RA area measurements all showed moderate correlation with invasive parameters (r = 0.45 to 0.66), manual (r = 0.36 to 0.57). Maximal RA area could accurately predict elevated mean RA pressure low and high-risk thresholds (area under the receiver operating characteristic curve artificial intelligence = 0.82/0.87 vs manual = 0.78/0.83), and predicted mortality similar to manual measurements, both p < 0.01. In the QC evaluation, artificial intelligence segmentations were suboptimal at 108/3795 and a low failure rate of 16/3795. In a subcohort (n = 1018), agreement by two QC observers was excellent, kappa 0.84. Conclusion: Automatic artificial intelligence CMR derived RA size and function are accurate, have excellent repeatability, moderate associations with invasive haemodynamics and predict mortality

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

A Fuzzy Logic Based Retrofit System for Enabling Smart Energy Efficient Electric Cookers

In recent years, our homes have been equipped with smarter and more energy efficient electric appliances such as smart fridges, washing machines, TVs, etc. However, it seems that cookers seem to have been left aside during this trend although for example in UK electric cookers consume up to 20% of the evening peak electricity consumption. In addition, over half of the accidental house fires are due to cooking and cooking appliances. One of the reasons for the lack of smart energy efficient electric cookers is the complexity of performing energy efficient control for the various cooking techniques. This paper presents a fuzzy logic based system which can be cheaply retrofitted in existing electric cookers to convert them to semi-autonomous, energy efficient and safe smart electric cookers. The proposed system can control the cooker heating plate to allow the semi-autonomous safe operation of the most common cooking techniques including boiling, stir/shallow-frying, deep-frying and warming. In addition, the developed system can identify when human intervention is necessary and when dangerous situations happen or are imminent. We will present several real-world experiments which were performed in the University of Essex intelligent apartment (iSpace) with various users where the proposed system operated a cooker semi-autonomously in various cooking modes and it was shown that when compared to the human manual operation, the proposed system realised an average energy saving of 21.42 %, 34.43% and 20.29% for the boiling, stir/shallow-frying and deep-frying cooking techniques respectively. In addition, the realised smart cooker has shown unique safety features not present in existing commercial cookers

A zSlices-based general type-2 fuzzy logic system for users-centric adaptive learning in large-scale e-learning platforms

Sophisticated educational technologies are evolving rapidly, and online courses are becoming more easily available, generating interest in innovating lightweight data-driven adaptive approaches that foster responsive teaching and improving the overall learning experience. However, in most existing adaptive educational systems, the black-box modeling of learner and instructional models based on the views of a few designers or experts tended to drive the adaptation of learning content. However, different sources of uncertainty could affect these views, including how accurately the proposed adaptive educational methods actually assess student responses and the corresponding uncertainties associated with how students receive and comprehend the resulting instruction. E-learning environments contain high levels of linguistic uncertainties, whereby students can interpret and act on the same terms, words, or methods (e.g., course difficulty, length of study time, or preferred learning style) in various ways according to varying levels of motivation, pre-knowledge, cognition, and future plans. Thus, one adaptive instructional model does not fit the needs of all students. Basing the instruction model on determining learners’ interactions within the learning environment in interpretable and easily read white-box models is crucial for adapting the model to students’ needs and understanding how learning is realized. This paper presents a new zSlices-based type-2 fuzzy-logic-based system that can learn students’ preferred knowledge delivery needs based on their characteristics and current levels of knowledge to generate an adaptive learning environment. We have evaluated the proposed system’s efficiency through various large-scale, real-world experiments involving 1871 students from King Abdulaziz University. These experiments demonstrate the proposed zSlices type-2 fuzzy-logic-based system’s capability for handling linguistic uncertainties to produce better performance, particularly in terms of enhanced student performance and improved success rates compared with interval type-2 fuzzy logic, type-1 fuzzy systems, adaptive, instructor-led systems, and non-adaptive systems