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In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate approach to employ. Additionally, determining the optimal input parameters for each AI technique remains a pertinent question in this domain. This study employs machine learning (ML) and artificial neural networks (ANN) to predict student grades within a programming tutoring system. The experiment involved university students whose interaction data with the e-learning system were analyzed and used for predictions. By identifying the structural relationships between the properties of the input data, this research aims to determine the most efficient AI method for accurately predicting student performance in e-learning systems. The structure of the input data in these systems is described by variables related to individual student activities, so correlations between variables were a natural starting point for further theoretical considerations. In this manner, by applying a filtering technique based on the minimum redundancy-maximum relevance (mrMR) criterion, it was shown that correlations among predictors and between predictors and the target variable play a significant role in defining the appropriate model for predicting student grades. The results showed that ANN (the Levenberg-Marquardt algorithm with Bayesian regularization) outperformed ML methods, achieving the highest prediction accuracy. The results obtained from this study can be of great importance for learning technologies engineering and AI in general.

Online learning systems should support students preparedness for professional practice by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students' development and engagement with programming is a challenging process. Early career computer science professionals are required not only to understand and master numerous programming concepts but also to efficiently learn how to apply them in different contexts. A prerequisite for an effective and engaging learning process is the existence of adaptive and flexible learning environments that are beneficial for both students and teachers. Students can benefit from personalized content adapted to their individual goals, knowledge, and needs; while teachers can be relieved from the pressure to uniformly and promptly evaluate hundreds of student assignments. This study proposes and puts into practice a method for evaluating learning content difficulty and students' knowledge proficiency utilizing a modified Elo-rating method. The proposed method effectively pairs learning content difficulty with students' proficiency, and creates personalized recommendations based on the generated ratings. The method was implemented in a programming tutoring system and tested with interactive learning content for object oriented-programming. By collecting quantitative and qualitative data from students who used the system for one semester, the findings reveal that the proposed method can generate recommendations that are relevant to students and has the potential to assist teachers in grading students by providing a more holistic understanding of their progress over time.

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non-technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student-facing MLA innovations.

With the wide expansion of distributed learning environments the way we learn became more diverse than ever. This poses an opportunity to incorporate different data sources of learning traces that can offer broader insights into learner behavior and the intricacies of the learning process. We argue that combining analytics across different e-learning systems can potentially measure the effectiveness of learning designs and maximize learning opportunities in distributed settings. As a step toward this goal, in this study, we considered how to broaden the context of a single learning environment into a learning ecosystem that integrates three separate e-learning systems. We present a cross-platform architecture that captures, integrates, and stores learning-related data from the learning ecosystem. To demonstrate the feasibility and the benefits of cross-platform architecture, we used regression and classification techniques to generate interpretable models with analytics that can be relevant for instructors in understanding learning behavior and sensemaking of the instructional method on learner performance. The results show that combining data across three e-learning systems improve the classification accuracy compared to data from a single learning system by a factor of 5. This article highlights the value of cross-platform learning analytics and presents a springboard for the creation of new cross-system data-driven research practices.

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system--"Protus", which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners' habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the "Protus" system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the "Protus" system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences. (Contains 7 tables and 12 figures.)

Aims: To compare the effects of intrathecal anaesthesia using procaine and xylazine, with and without sedation with I/V xylazine and butorphanol, on sedation and cardiorespiratory measures in calves undergoing umbilical surgery. Methods: Male dairy calves, aged <3 months, were recruited that had enlargement of the umbilical stalk which was abnormal when palpated. They were assigned to receive either intrathecal injection between the sixth lumbar and first sacral vertebrae of 4 mg/kg of 2% procaine and 0.2 mg/kg 2% xylazine, with I/V injection of 0.02 mg/kg xylazine and 0.1 mg/kg of butorphanol (IT + SED group; n = 6), or the same intrathecal injection and I/V injection of 0.9% saline (IT group; n = 7). Surgery to correct abnormalities was carried out with calves positioned in dorsal recumbency. Rescue analgesia with injections of 2% procaine around the surgical wound was administered when movements triggered by surgery were observed. Post-operative analgesia was provided using I/V 0.5 mg/kg meloxicam. Duration of surgery was recorded, as well as degree of sedation, heart rate, systolic (SAP), diastolic (DAP) and mean (MAP) arterial blood pressure during surgery. Results: All anaesthetic and surgical procedures were successfully performed. Mean total duration of surgery was similar for the IT + SED and the IT groups (30.33 (SD 10.09) and 31.00 (SD 10.21) minutes, respectively) (p = 0.92). All calves were at least mildly sedated from 5 minutes after injections to the end of the surgery. One calf in the IT + SED group and three calves in the IT group required rescue analgesia when the umbilicus was manipulated. Between 0 and 10–15 minutes after injection, decreases in mean heart rate, SAP, MAP and DAP were observed in both groups. Mean SAP was lower in the IT + SED than the IT group. Hypotension (MAP<60 mm Hg) was present in four calves from the IT + SED group and in one from the IT group. Conclusions and clinical relevance: Intrathecal administration of 2% procaine and 2% xylazine allowed the successful completion of umbilical surgery, but 30% of calves needed rescue analgesia during surgery. Clinically, the addition of I/V sedation seemed to provide better analgesia than intrathecal block alone but resulted in greater hypotension. [ABSTRACT FROM AUTHOR]

Abstract Objective Diabetes is a complex disease implicating several organs and cell types. Within the islets, dysregulation occurs in both alpha- and beta-cells, leading to defects of insulin secretion and increased glucagon secretion. Dysregulation of alpha-cells is associated with transcriptome changes. We hypothesized that microRNAs (miRNAs) which are negative regulators of mRNA stability and translation could be involved in alpha-cell alterations or adaptations during type 2 diabetes. Methods miRNA microarray analyses were performed on pure alpha- and beta-cells from high-fat diet fed obese hyperglycemic mice and low-fat diet fed controls. Then, the most regulated miRNA was overexpressed or inhibited in primary culture of mouse and human alpha-cells to determine its molecular and functional impact. Results 16 miRNAs were significantly regulated in alpha-cells of obese hyperglycemic mice and 28 in beta-cells. miR-132-3p had the strongest regulation level in alpha-cells, where it was downregulated, while we observed an opposite upregulation in beta-cells. In vitro experiments showed that miR-132-3p, which is inversely regulated by somatostatin and cAMP, is a positive modulator of alpha-cell proliferation and implicated in their resistance to apoptosis. These effects are associated with the regulation of a series of genes, including proliferation and stress markers Mki67 and Bbc3 in mouse and human alpha-cells, potentially involved in miR-132-3p functions. Conclusions Downregulation of miR-132-3p in alpha-cells of obese diabetic mice may constitute a compensatory mechanism contributing to keep glucagon-producing cell number constant in diabetes. Highlights • Alpha- and beta-cells present specific microRNA signatures. • 16 microRNAs are significantly regulated in alpha-cells of obese hyperglycemic mice. • miR-132-3p is downregulated in alpha-cells of obese hyperglycemic mice. • miR-132-3p stimulates alpha-cells proliferation and resistance to apoptosis. • miR-132-3p is regulated by somatostatin in alpha-cells. [ABSTRACT FROM AUTHOR]