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The importance of using accessible learning objects in order to ensure access to education for all students is notable. The present work sought to bring an overview of the research on accessible learning objects for the visually impaired based on the systematic literature review methodology. Then, 620 articles from five scientific databases were retrieved, of which 44 were considered for analysis. The results showed that the research question and sub-question were answered, and it was possible to perceive the support that distance education tools can provide if they are accessible. Finally, the various ways in which the researchers approached the accessibility of each learning object were identified and the more specific models, techniques and computational tools that exist and are used in the context of education were detected in order to promote accessibility for people with visual impairments. [ABSTRACT FROM AUTHOR]

Student retention is a significant challenge for higher education institutions (HEIs). The fact that a considerable number of dropouts from universities are primarily due to academic underperformance motivates universities to develop learning analytics tools based on models for predicting learning success. However, the scalability of such models is limited since students' academic performance and engagement, as well as the factors influencing them, are largely determined by the educational environment. The article proposes a hybrid approach to forecasting success in completing an academic semester, which involves creating a set of predictive models. Some of the models use historical student data, while others are intended to refine the forecast using current data on student performance and engagement, which are regularly extracted from available sources. Based on this approach, we developed an ensemble of machine learning models and the Markov-process model that simultaneously address the tasks of forecasting success in mastering a course and success in completing a semester. The models utilize digital footprint data, digital educational history, and digital personality portraits of students extracted from the databases of Siberian Federal University, and the resulting ensemble demonstrates a high quality of the forecast. The proposed approach can be utilized by other HEIs as a framework for creating mutually complementary forecasting models based on different types of accessible educational data. [ABSTRACT FROM AUTHOR]

Assessing the usefulness of reviews has been the aim of several research studies. However, results regarding the significance of usefulness determinants are often contradictory, thus decreasing the accuracy of reviews' helpfulness estimation. Also, bias in user reviews attributed to differences, e.g., in gender, nationality, etc., may result in misleading judgments, thus diminishing reviews' usefulness. Research is needed for sentiment analysis algorithms that incorporate bias embedded in reviews, thus improving their usefulness, readability, credibility, etc. This study utilizes fuzzy relations and fuzzy synthetic evaluation (FSE) in order to calculate reviews' usefulness by incorporating users' biases as expressed in terms of reviews' articulacy and sentiment polarity. It selected and analyzed 95,678 hotel user reviews from Tripadvisor, written by users from five specific nationalities. The findings indicate that there are differences among nationalities in terms of the articulacy and sentiment of their reviews. The British are most consistent in their judgments expressed in titles and the main body of reviews. For the British and the Greeks, review titles suffice to convey any negative sentiments. The Dutch use fewer words in their reviews than the other nationalities. This study suggests that fuzzy logic captures subjectivity which is often found in reviews, and it can be used to quantify users' behavioral differences, calculate reviews' usefulness, and provide the means for developing more accurate voting systems. [ABSTRACT FROM AUTHOR]

As it concerns remote laboratories (RLs) for teaching microcontroller programming, the related literature reveals several common characteristics and a common architecture. Our search of the literature was constrained to papers published in the period of 2020–2023 specifically on remote laboratories related to the subject of teaching microcontroller programming of the Arduino family. The objective of this search is to present, on the one hand, the extent to which the RL platform from the Hellenic Mediterranean University (HMU-RLP) for Arduino microcontroller programming conforms to this common architecture and, on the other hand, how it extends this architecture with new features for monitoring and assessing users' activities over remote labs in the context of pervasive and supervised learning. The HMU-RLP hosts a great number of experiments that can be practiced by RL users in the form of different scenarios provided by teachers as activities that users can perform in their self-learning process or assigned as exercises complementary to the theoretical part of a course. More importantly, it provides three types of assessments of the code users program during their experimentation with RLs. The first type monitors each action users perform over the web page offered by the RL. The second type monitors the activities of users at the hardware level. To this end, a shadow microcontroller is used that monitors the pins of the microcontroller programmed by the users. The third type automatically assesses the code uploaded by the users, checking its similarity with the prototype code uploaded by the instructors. A trained AI model is used to this end. For the assessments provided by the HMU-RLP, the experience API (xAPI) standard is exploited to store users' learning analytics (LAs). The LAs can be processed by the instructors for the students' evaluation and personalized learning. The xAPI reporting and visualization tools used in our prototype RLP implementation are also presented in the paper. We also discuss the planned development of such functionalities in the future for the use of the HMU-RLP as an adaptive tool for supervised distant learning. [ABSTRACT FROM AUTHOR]

The increasing demand for freight services and the use of larger vessels to meet this demand has led to challenges related to storage space and logistics activities, highlighting the need for improvements in port infrastructure for better logistics management. At a crucial phase in its growth, the Port of Trois-Rivières in Canada is planning a major expansion, including the construction of a new terminal to enhance its hosting capacities and freight services. This expansion faces potential access congestion problems during the planned construction, exacerbated by the port's urban setting. In response to the needs identified by the port authorities for this event, the study's objective is to assess the implications of increased construction and freight truck flows on access gate fluidity and the impact of additional access infrastructure investment to mitigate potential congestion. These evaluations aim to define effective access management strategies throughout the construction period of the new terminal. To address these complexities, our approach is based on scenario analysis in variants co-constructed with the partner. These scenarios are evaluated using simulation models, configured according to parameters calibrated with a granularity that allows congestion detection. The results enabled an evaluation of the capability of existing and potential gates to manage access. Subsequently, recommendations were shaped in accordance with the expected objectives to manage access traffic effectively. These recommendations concern the optimization of construction activity planning, the layout and planning of access, and the importance of enhanced collaboration between municipal and port authorities for more controlled road traffic management. Recognizing the importance of synchromodality, road network centrality management, and the outsourcing of capacity through inter-port cooperation and with dry ports to manage congestion, these tools will be discussed in this work. The study proposes an approach that reconciles scientific rigor with the implementation constraints of the proposed solutions, allowing this study wider applicability in various port contexts facing challenges in this field of study. [ABSTRACT FROM AUTHOR]

The research on student dropout demonstrates that there is no consensus on its definition and scope. Although there is an expanding collection of research on the topic, student dropout remains a significant issue, characterized by numerous uncertainties and ambiguous aspects. The primary aim of this investigation is to assess the research trends of student dropout within the distance education literature by employing data mining and analytic approaches. To identify these patterns, a total of 164 publications were examined by applying text mining and social network analysis. The study revealed some intriguing facts, such as the misinterpretation of the term "dropout" in different settings and the inadequacy of nonhuman analytics to explain the phenomenon, and promising implications on how to lessen dropout rates in open and distance learning environments. Based on the findings of the study, this article proposes possible directions for future research, including the need to provide a precise definition of the term "dropout" in the context of distance learning, to develop ethical principles, policies, and frameworks for the use of algorithmic approaches to predict student dropout, and finally, to adopt a human-centered approach aimed at fostering learners' motivation, satisfaction, and independence to reduce the rate of dropout in distance education. [ABSTRACT FROM AUTHOR]

Seed vigor is an important aspect of seed quality. High-vigor seeds show rapid and uniform germination and emerge well, especially under adverse environmental conditions. Here, we determined hydrotime model parameters by incubating seeds at different water potentials (0.0, −0.2, −0.4, −0.6, and −0.8 MPa) in the laboratory, for 12 seed lots of Chinese milk vetch (Astragalus sinicus) (CMV), a globally important legume used as forage, green manure, and a rotation crop. Pot experiments were conducted to investigate the seedling emergence performance of 12 CMV seed lots under control, water stress, salinity stress, deep sowing, and cold stress conditions. Meanwhile, the field emergence performance was evaluated on two sowing dates in June and October 2022. Correlation and regression analyses were implemented to explore the relationships between hydrotime model parameters and seedling emergence performance under various environmental conditions. The seed germination percentage did not differ significantly between seed lots when seeds were incubated at 0.0 MPa, whereas it did differ significantly between seed lots at water potentials of −0.2, −0.4, and −0.6 MPa. The emergence percentage, seedling dry weight, and simplified vigor index also differed significantly between the 12 seed lots under various environmental conditions. Ψb(50) showed a significant correlation with germination and emergence performance under various environmental conditions; however, little correlation was observed between θH or σφb and germination and emergence. These results indicate that Ψb(50) can be used to estimate seed vigor and predict seedling emergence performance under diverse environmental conditions for CMV and similar forage legumes. This study will enable seed researchers, plant breeders, and government program directors to target higher seed vigor more effectively for forage legumes. [ABSTRACT FROM AUTHOR]

Over the years, there has been heightened interest in the health benefits of n-3 polyunsaturated fatty acids (PUFA) in reducing chronic diseases such as, cardiovascular disease (CVD), cancer, type 2 diabetes, and acute macular degeneration (AMD). Due to inconsistent findings in the evidence, a review to critically examine the plethora of evidence from randomized controlled trials (RCTs) in n-3 PUFA research was undertaken. The aim of this review is to study the highest level of evidence and to identify gaps in n-3 PUFA research. RCTs were originally designed for pharmaceutical research and later adopted for nutrition and food-related research. RCTs with active diseases assume that n-3 PUFA will have "drug" like effects, and this high expectation may have led to the inconsistent evidence in the literature. The inconsistency in the literature may be related to varying doses of n-3 PUFA, sources of n-3 PUFA (food vs. supplement; plant vs. marine), type of n-3 PUFA (mixture vs. purified), trial duration, population characteristics, sample size, and genetic variation. For future research, there is a need to distinguish between primary and secondary prevention, and to focus RCTs on primary prevention of chronic diseases by n-3 PUFA which is lacking in the literature. [ABSTRACT FROM AUTHOR]

Personalised adaptive learning is becoming increasingly popular as a method of providing each student on an online platform with learning experiences that are tailored to their own requirements and peculiarities. This enables learners to go along many learning routes with the shared objective of information and skill development. In such systems, adaptivity and intelligence play distinct roles, with adaptivity being a more data-driven decision-making approach and intelligence being the emulation of human traits in a learning setting. Pedagogical agents, as defined in the field of artificial intelligence, are virtual characters with anthropomorphic features that are introduced for educational reasons. Because e-learning is a continuously growing area, the responsibilities of pedagogical agents change based on the goals that have been established for them. This article provides a systematic evaluation of pedagogical agents' research and empirical data in e-learning from 2015 to 2022. Their responsibilities will be examined specifically in terms of flexibility and variety, realistic simulation, and their influence on learning: performance improvement, improved motivation, and engagement. The article finishes with a discussion and recommendations on pedagogical agents' future directions in this ever-changing world of individualised adaptive e-learning. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature's themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Students have learning difficulties, mainly in processes that involve attention and interpretation of written or spoken language. Technological tools allow to create computational platforms with adaptation aspects depending on the student's characteristics. It is also important to highlight the progress of the measurement of cognitive processes such as attention through NeuroSky's MindWave EEG sensors. This paper aims to present the results of analyzing attention levels of children with learning difficulties, based on the acquired brain waves. As a final result, an adaptive computational system that displays educational activities regarding educational profiles of children is obtained. Design/methodology/approach: The Kamachiy–Idukay platform was chosen to make the validation. The platform generates the educational activities according to the students' profile. The validation phases were identification of the test environment, the first environment required a scenario that involved students with learning difficulties, to verify the functionality of the system, when analyzing cases of the students with learning difficulties; identification of two validation criteria, type of educational activity and attention difficulties of the students; and analysis of the brain signal when children interact with the educational content. Findings: The adaptation of contents that include music and animations generate higher levels of attention in students with difficulty. The analysis of signals from the NeuroSky sensor to determine the attentional levels in children allowed a generation of content adapted to the characteristics of the difficulty in each child. Research limitations/implications: For the validation, it was necessary at the beginning of the activity to determine the stability of the signal emitted by the NeuroSky sensor. Two cases were studied in children with difficulty and their measure of attention versus adaptive contents. Practical implications: A k-means algorithm was used to establish the attention levels of the children. Social implications: Children with learning difficulties have different learning styles, which implies an adaptation of content that generates an attentional process according to their characteristics. Originality/value: Evaluation content adaptation taking into account the signal brain sensor NeuroSky for learning process. The signal brain of the student when interacting with the activities is include in the student profile. [ABSTRACT FROM AUTHOR]

Improvement in high-throughput MALDI-TOF MS analysis requires practical and efficient sample preparation protocols for high acquisition rates. The use of hexagonal mesoporous silica (HMS) sorbents in combination with MALDI-TOF MS was explored as a versatile tool for peptidomic profiling of clinical specimens difficult to process, but considered important sources of disease biomarkers: synovial fluid and sputum. A rapid and efficient procedure, based on dispersive solid-phase extraction of peptides using commercially available wormhole mesostructured HMS, was tested for: a) pre-concentration of standard peptides in serially diluted solution up to the sub-nanomolar range; b) peptidome profiling of sputum and synovial fluid. The use of HMS, as dispersed sponges, significantly amplified the peptidic repertoire of sputum and synovial fluid by excluding from the adsorptive process large size proteins, which mask and/or suppress peptidome signals. The protocol proposed, as dispersive solid phase extraction, ensures good analytical performances. Moreover, it is economical and rapid, as it avoids the use of less reproducible and prolonged sample preparation procedures, such as the use of ultrafiltration filter devices. These findings may contribute to defining a high-throughput screening MS-based platform for monitoring key peptidic features of difficult to analyse bodily fluids in a clinical setting. [ABSTRACT FROM AUTHOR]