MIIAM: An Algorithmic Model for Predicting Multimedia Effectiveness in eLearning Systems

From a system’s theoretical point of view, adaptive learning systems (ALS) for education and training contain in their core – in a simplified form – closed feedback control loops in which the control is determined by the measured users’ performance. Improving this performance can increase the learning outcome, especially for critical disciplines such as education or training for disaster risk management. However, for this special form of intelligent (e-learning) assistance systems, learning theories and behavioral models have to be considered, e.g., game flow theory, cognition models, or learning models. The research question is how adaptive interactive learning environments (ILE) such as serious games and computer simulations can be characterized and analyzed to determine optimal adaptation strategies. Adaptive learning environments should adapt to the context-related needs of the user in order to ensure and optimize learning success, especially for disaster management training. This contribution presents a concept for an interoperable, adaptive ILE framework which follows control theory and its models, contributing to the state of the art for adaptive games or simulations in disaster risk management.

Identification of personal traits in adaptive learning environment: Systematic literature review

Evaluation of Preceding Variables Affecting Behavioral Use and Acceptance of Chord-Enabled Keyboard Among Students

This study investigates the transformative potential of integrating Artificial Intelligence (AI), animation, and personalized learning in contemporary education. Employing a mixed-methods approach involving interviews and experimental manipulations, the research examines the interconnectedness of these three domains and their collective impact on student engagement and English reading comprehension learning outcomes. The study employed a quasi-experimental design. Participants engaged in an 8-week AI-driven personalized learning intervention that incorporated animated content, with pre- and post-test assessments evaluating reading comprehension outcomes. Qualitative data from interviews provided further insight into the impact of animation and personalized learning on student engagement and comprehension. Findings reveal that AI-driven personalized learning, coupled with engaging animations, leads to enhanced learner motivation, improved knowledge retention, and higher academic performance. The integration of these elements creates an inclusive and adaptable learning environment, addressing the diverse needs of students. This research highlights the significance of interdisciplinary collaboration in educational innovation and emphasizes the vast potential that lies at the intersection of AI, animation, and personalized learning to revolutionize 21st-century education.

Effects of pacing and cognitive style across dynamic and non-dynamic representations

Since 1980, obesity has increasingly become a global health issue—it is increasingly prevalent among children with serious implications for their health and their later well-being as adults. The World Health Organization considers obesity a non-communicable disease which may lead to severe diseases such as diabetes, cardiovascular diseases, and various kinds of cancers. Detrimental dietary patterns and the absence of physical activity are the root causes of obesity. Data mining is a quickly developing field with numerous applications in the field of medical services. One of its several utilizations is the forecasting of sicknesses. The proposed work intends to examine varied machine learning techniques: K- Nearest Neighbor, Decision Tree, Ensemble Random Forest, and Deep Neural Network to prognosticate obesity. The inference analysis shows that the ensemble random forest classification with XG Boosting is the most precise procedure to prognosis obesity.KeywordsObesityNon-communicable diseaseData miningEnsemble random forestDeep neural network

Landslide disaster risk reduction necessitates the investigation of different geotechnical causal factors for slope failures. Machine learning (ML) techniques have been proposed to study causal factors across many application areas. However, the development of ensemble ML techniques for identifying the geotechnical causal factors for slope failures and their subsequent prediction has lacked in literature. The primary goal of this research is to develop and evaluate novel feature selection methods for identifying causal factors for slope failures and assess the potential of ensemble and individual ML techniques for slope failure prediction. Twenty-one geotechnical causal factors were obtained from 60 sites (both landslide and non-landslide) spread across a landslide-prone area in Mandi, India. Relevant causal factors were evaluated by developing a novel ensemble feature selection method that involved an average of different individual feature selection methods like correlation, information-gain, gain-ratio, OneR, and F-ratio. Furthermore, different ensemble ML techniques (Random Forest (RF), AdaBoost (AB), Bagging, Stacking, and Voting) and individual ML techniques (Bayesian network (BN), decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM)) were calibrated to 70% of the locations and tested on 30% of the sites. The ensemble feature selection method yielded six major contributing parameters to slope failures: relative compaction, porosity, saturated permeability, slope angle, angle of the internal friction, and in-situ moisture content. Furthermore, the ensemble RF and AB techniques performed the best compared to other ensemble and individual ML techniques on test data. The present study discusses the implications of different causal factors for slope failure prediction.

Context. Image segmentation plays a key role in computer vision. The quality of segmentation is affected by many factors: noise, artifacts, complex shapes of objects. Classical methods cannot always guarantee good success, depending on the quality of the image and the existing noise, they cannot always achieve the desired result. The proposed method uses an ensemble of neural networks, which makes it possible to increase the accuracy and stability of segmentation. Objective. The goal of the work is to develop a new method of combining predictions of neural network ensembles, which can improve segmentation accuracy by combining images of different image sizes. Method. A method is proposed that averages the shapes of objects depicted on prediction masks. A pyramid of images is used to improve segmentation quality, each level of the pyramid corresponds to an increased size of the original image. This approach allows obtaining image characteristics at different levels. For a test image, a prediction is obtained from each neural network in the ensemble, after which a pyramid is built for the image. All pyramid levels are combined into the final image using SAAMC. All obtained final images for each neural network are also combined at the end using SAAMC. The use of an ensemble of neural networks combined with the pyramid method allows for reducing the impact of noise and artifacts on the segmentation results. Results. The use of this method was compared with the usual use of individual neural networks and the ensemble averaging method. The obtained results show that the proposed method outperforms its competitors. Application of the proposed method improved the accuracy and quality of segmentation. Conclusions. The conducted research confirmed the sense of using an ensemble of neural networks and creating a new method of combining predictions. The use of an ensemble of neural networks makes it possible to compensate for the errors and shortcomings of individual neural networks. Using the proposed method can significantly reduce the impact of noise and artifacts on segmentation. Further study and modification of this method will make it possible to further improve the quality of segmentation.

Development of novel computational approaches for modeling protein properties from their primary structure is a main goal in applied proteomics. In this work, we reported the extension of the autocorrelation vector formalism to amino acid sequences for encoding protein structural information with modeling purposes. Amino Acid Sequence Autocorrelation (AASA) vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. A total of 720 AASA descriptors were tested for building predictive models of the thermal unfolding Gibbs free energy change of human lysozyme mutants. In this sense, ensembles of Bayesian-Regularized Genetic Neural Networks (BRGNNs) were used for obtaining an optimum nonlinear model for the conformational stability. The ensemble predictor described about 88% and 68% variance of the data in training and test sets, respectively. Furthermore, the optimum AASA vector subset was shown not only to successfully model unfolding thermal stability but also to distribute wild-type and mutant lysozymes on a stability Self-organized Map (SOM) when used for unsupervised training of competitive neurons.

The purpose of this study is to explore the value of Al-assisted project-based learning design in Chinese language education. Through instructional design that integrates foreign language, cross-cultural understanding, and multimedia expression, we aim to enhance learners' proactive attitudes and improve learning outcomes using Al and ICT. Conducted the tourism Chinese course at a Japanese university, the research employed mixed methods to assess the effectiveness of integrated PBL. The course utilized Al and ICT tools such as an online whiteboard, a pronunciation practice tool, generative Al for writing assistance, and a video editing application. Data from student surveys, feedback, and teacher observations indicated that approximately half of the students found the Al-supported PBL effective for their learning needs, especially in pronunciation practice and video editing, though it didn't fully address speaking skill inaccuracies. Al and ICT tools were effective in supporting cultural knowledge and intercultural communication skills, but student evaluation of Al's impact on learning were polarized. The value of the CFL approach using Al and ICT was discovered in creating immersive, adaptive learning environments—a value that had gone unnoticed in traditional teaching and learning methods—while also underscoring the need for proper support and training from teachers. Future challenges include integrating Al literacy into language courses and addressing varying technological proficiency levels among students.

Studies show that an adaptive learning environment is needed by learners. Adaptive e-learning systems have been developed with personalized instructions based on student's knowledge level. Since each student has a unique cognitive and metacognitive level, we currently develop an adaptive Hypermedia Learning Environment (HLE) for Algorithm and Data Structure Course based on a metacognitive awareness level. HLE provides adaptive scaffolding as instructional interventions to facilitate students' aptitude in programming. Scaffolding can be determined by knowing student's metacognitive awareness level. In this research, we propose the use of Tahani Model of Fuzzy Database in web-based HLE to support the adaptive decision-making. Tahani is one of some feasible models in a fuzzy database that can categorize students' metacognitive awareness level. The produced fuzzy rules have been validated by metacognitive experts of educational psychology. A hundred students have been categorized into three groups (low, medium, high) and provided with appropriate scaffolding. Evaluation results verified the suitability of the proposed method and the student's scaffolding.

Due to the rapid emergence of multi-drug resistant (MDR) bacteria, existing antibiotics are becoming ineffective. So, researchers are looking for alternatives in the form of antibacterial peptides (ABPs) based medicines. The discovery of novel ABPs using wet-lab experiments is time-consuming and expensive. Many machine learning models have been proposed to search for new ABPs, but there is still scope to develop a robust model that has high accuracy and precision. In this work, we present StaBle-ABPpred, a stacked ensemble technique-based deep learning classifier that uses bidirectional long-short term memory (biLSTM) and attention mechanism at base-level and an ensemble of random forest, gradient boosting and logistic regression at meta-level to classify peptides as antibacterial or otherwise. The performance of our model has been compared with several state-of-the-art classifiers, and results were subjected to analysis of variance (ANOVA) test and its post hoc analysis, which proves that our model performs better than existing classifiers. Furthermore, a web app has been developed and deployed at https://stable-abppred.anvil.app to identify novel ABPs in protein sequences. Using this app, we identified novel ABPs in all the proteins of the Streptococcus phage T12 genome. These ABPs have shown amino acid similarities with experimentally tested antimicrobial peptides (AMPs) of other organisms. Hence, they could be chemically synthesized and experimentally validated for their activity against different bacteria. The model and app developed in this work can be further utilized to explore the protein diversity for identifying novel ABPs with broad-spectrum activity, especially against MDR bacterial pathogens.

The emerging trend for taking a more collaborative approach to life-long learning is picking up pace. So, too, is the rate at which online learning systems are implemented by novice courseware designers. Nevertheless, if we want to sustain the momentum, we must understand more about how to manage the computer–human interaction (CHI) and, henceforth, the cognitive skills acquisition process involved in E-learning systems (McKay & Garner, 1999). Once we understand the CHI phenomenon, learn how to manage the E-learning environment successfully, manage media in an efficient and effective manner, conquer collaborative online communication and knowledge-sharing, we may be able to claim that context-mediated learning has arrived. This paper discusses an examination of the contextual issues involved in understanding the interactivity of instructional conditions and cognitive style as a metaknowledge acquisition process. Meta-knowledge is used here to convey knowledge about knowledge. This distinction is necessary to differentiate between the more common usage of the term, whereby knowledge is generated by an individual presented with various pieces of information. Further work is needed to substantiate the speculated mechanisms relating to the relationship between specific learning domains, and notational transfer (internal/external exchange process) in an online learning context.

The reliable prediction of dissolved oxygen concentration (DO) is significantly crucial for protecting the health of the aquatic ecosystem. The current research employed four different single AI-based models, namely long short-term memory neural network (LSTM), extreme learning machine (ELM), Hammerstein-Weiner (HW) and general regression neural network (GRNN) for modeling the DO concentration of Kinta River, Malaysia using available water quality (WQ) parameters. Afterwards, the first scenario used four different ensemble techniques (ET). Two linear, i.e. simple averaging ensemble (SAE) and weighted averaging ensemble (WAE) and two nonlinear namely; backpropagation neural network ensemble (BPNN-E) and HW ensemble (HW-E). The second scenario employed a hybrid random forest (RF) ensemble in order to enhance the prediction accuracy of the single models. The WQ parameters were subjected to a different pre-analysis test to ascertain their stability. The four-model combinations are generated using the nonlinear sensitivity input selection approach. The modeling performance was assessed using the statistical measures of Nash-Sutcliffe coefficient efficiency (NSE), Willmott's index of agreement (WI), root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE) and correlation coefficient (CC). The results of the single AI-based models demonstrated that HW (M3) served as the best model for predicting DO concentration. For ensemble results, BPNN-E (WI = 0.9764) was superior to the other three ET with average decreased of more than 2% with regards to MAE. Investigation on the hybrid RF ensemble demonstrated the reliable accuracy for all the hybrid models with better predictive skill shown by the HW-RF (CC = 0.981) ensemble. The overall results verified the promising impact of HW-M3, ET and hybrid RF ensemble for the prediction of the DO concentration in the Kinta River, Malaysia.

This chapter provides an overview of different models and theories on learning with multiple representations and outlines their importance for physics education. We distinguish between internal and external multiple representations. The latter refer to any combination of visible representations such as pictures, text, graphs, and tables. Physics can be seen as a visual subject and such external representations have an unquestionable relevance in physics education and are described in theories such as the Cognitive Theory of Multimedia Learning, the Integrated Model of Text and Picture Comprehension, and the DeFT framework for learning with multiple representations. Each theory is discussed in this chapter. The benefits of instructional design for learning, that is, the nature of such representations and how they are combined with each other are moderated by individual learner characteristics, some of which are described in the next part of the chapter. The final emphasis on internal representations refers to the way in which external representations are translated into internal mental models: an example used is the Theory of Choreographies of Teaching which closes the chapter.