Learning Progress Research Articles

Effects of formative feedback on argumentative writing in English and cross-linguistic transfer to German

BackgroundArgumentative writing is a crucial but challenging competence for students. Process-oriented teaching with formative feedback benefits writing, yet the effects of feedback based on rubrics and exemplars versus in-text comments, remains unclear. MethodWe conducted a randomized controlled intervention study with 294 secondary students of English as a foreign language to examine the effects of formative feedback on argumentative writing and genre knowlege and investigate cross-linguistic transfer to German. Feedback was implemented within a learning unit on argumentative writing that we had developed for this project. The experimental groups (EG1: rubric + exemplar; EG2: in-text comments; EG3: rubric + exemplar and in-text comments) were compared to two control groups (CG1: learning unit without additional feedback; CG2: no intervention). We assessed writing quality (pre-, post-, and follow-up tests: 1122 essays in English; pre- and post-tests: 588 essays in German) and genre knowledge; feedback perceptions were measured by questionnaires. ResultsANOVA results showed significant gains in English writing quality in the EGs and CG1, and the EGs made significant progress regarding genre knowledge. EG1 made large gains. Results were largely sustained. Regression analyses revealed learning progress in English in the EGs and CG1 as a significant predictor of writing quality in German at T2. There were no significant group differences in students’ perceptions of feedback. ConclusionOur results indicate that feedback based on rubrics and exemplars, which can easily be implemented in larger classes, promotes writing and genre knowledge and is perceived as helpful by students.

Online learning system for English speech automatic recognition based on hidden Markov model algorithm and conditional random field algorithm

E-learning provides a flexible way of learning, which can provide corresponding learning content and tasks according to students’ interests, abilities and learning progress, so that students can choose learning content and learning progress more independently. In traditional teaching methods, teachers need to spend a lot of time and energy to evaluate students’ learning. The e-learning system can automatically record students’ learning behavior and learning results, and give feedback and evaluation in time during the learning process. Automatic speech recognition systems have been widely applied in various fields, and the demand for more user-friendly and convenient user operating systems is becoming increasingly urgent. This article proposes an English automatic speech recognition system based on hidden Markov model algorithm and conditional random field algorithm to improve recognition accuracy and stability, and applies it to the computer field. Label inference and optimization for speech recognition were carried out using the conditional random field algorithm. By establishing the correlation between features and labels, label inference was performed on the speech signal, which was compared with existing speech recognition systems to evaluate whether the recognition accuracy and stability of the new system had been improved. The results show that the English automatic speech recognition system based on hidden Markov model algorithm and conditional random field algorithm exhibits high performance in terms of recognition accuracy and stability. Compared with traditional speech recognition systems, this system has made significant improvements in the application of computer science.

The impact of COVID-19 on medical education and training.

The COVID-19 pandemic social isolation policies have accelerated the shift to online teaching for medical students and doctors in training worldwide. Online learning is cost-effective, available, and flexible. However, it can be challenging due to the technical system errors, which results in the disruption of the learning process and social isolation yielding to less satisfaction among students and teachers. The above can have negative consequences on the mental health of medical students and trainees, which is an under-researched area. United Kingdom based medical students and doctors in speciality training encountered disruptions to medical education and training due to the pandemic. Medical school and deaneries had to endorse adjustments to teaching and training delivery methods, examination, and assessments to ensure the continued progression of learning and training. A successful e-learning model depends on motivated and well-prepared medical students and teachers and structured educational materials in supported learning environment and institutions. A blended model is likely to be utilised by medical institutions for medical training in the future, which will need to be researched.

Co-created augmented reality app and its impact on the effectiveness of learning a foreign language and on cultural knowledge

This paper addresses the limited exploration of the impact of virtual and augmented reality on the learning process, particularly within the context of foreign language learning among young learners. Motivated by this research gap, the EULALIA, conducted from 2019 to 2023 (2019-1-IT02-KA203-063228), sought to evaluate the efficacy of integrating technology in language education. Specifically, the study investigates the influence of virtual and augmented reality on foreign language learning, examining its effects on subjectively assessed cultural knowledge, and workflow during classes, as perceived by educators. Involving 239 learners, the study employed a pre/post survey methodology, wherein participants provided feedback before and after working with the EULALIA application or traditional learning methods. The quantitative analysis of survey data was complemented by semi-structured interviews with participating teachers, providing additional insights. The results indicate a statistically significant increase in cultural knowledge among the group utilizing augmented reality. The findings suggest a positive impact on students’ learning progression, and workflow during classes. This research contributes valuable insights into the potential of integrating virtual and augmented reality in language education, offering a foundation for future pedagogical advancements.

The Impact of Integration of E-Learning in Science: Applying Technology in Biology Classes as a Model.

Background The most common teaching method in science is the lecture-based approach. E-learning is one of the advanced teaching methodologies that have gained attention in recent decades. It was hypothesized that the intervention of e-learning improved students' scores and satisfaction in biology classes. The purpose of this study was to determine if e-learning combined with traditional learning methods of lectures would improve unified students' scores and satisfaction in biology classes. Methodology The study design was a quasi-experimental approach. This study was conducted at the College of Science and Health Professions Jeddah, King Saud bin Abdulaziz University for Health Sciences, Jeddah,Kingdom of Saudi Arabia. The study subjects comprised first-year female students (pre-professional program) in two different classes. The total number of participants was 150 (75 students in each group). The systematic random sampling technique was used. Randomly, one class (experimental group) received e-learning in addition to traditional lectures, and the other group (control group) received traditional lecture. The "Biology for Health Sciences" course was selected for this purpose. Scientific pre-test and post-tests were performed, and a satisfaction questionnaire was filled in for both groups. Results We found that e-learning and traditional lecture improved students' scores (P<0.01). Learning progress in the e-learning group was significantly more than that in the control group (P<0.05). Lecture has been shown to increase learning gains by 33%, and the integration of e-learning increased learning gains by 62%. Analysis of questionnaire results showed improved student satisfaction with the course in the study group. Conclusion The integration of e-learning approach significantly improved the retention and students' scores and satisfactions. E-learning could be applied more in pre-professional year science courses. According to these advantages, the quality of science education can be improved with this approach.

Assessment of students by using tools to assess the holistic performance of students at primary level of students

This research paper provides a comprehensive review of Assessment of students during learning process in a classroom. Assessment play a crucial role in facilitating the teaching and learning process by providing resources that engage students, enhance understanding, conceptual clearity, critical thinking, abstract thinking, feedback, improvement of students learning process in a classroom and promote active learning. This research paper explores various types of tools and their characteristics, design principles, and their effectiveness in diverse educational settings. Additionally, it examines the role of using tools in creating and utilizing assessment tools as well as the importance of aligning with achieve curriculum goals and student needs. The paper concludes with recommendations for educators and policymakers to maximize the potential of assessment to improving student learning progress.

Knowledge ontology enhanced model for explainable knowledge tracing

Knowledge Tracing (KT) aims to predict learners’ future learning outcomes based on their past learning interactions. Deep Knowledge Tracing (DKT) is a technology developed in recent years that employs deep learning techniques to dynamically track students’ learning progress and offer personalized learning support. Yet, existing research often miss the complex interplay between student characteristics and knowledge components, limiting interpretability and adaptability in assessments. Addressing this gap, we introduce the Ontology Perceptible Knowledge Tracing (OPKT) model, a novel approach that reconceptualizes student-knowledge interaction by integrating both student and knowledge ontologies directly into the tracing process. This integration allows for a more nuanced representation of learning interactions, significantly enhancing the predictive precision and interpretability of the model. The OPKT model extracts and utilizes ontology features—deriving student attributes from behavioral data and aligning them with knowledge aspects through sophisticated relationship mapping. This dual-layer integration captures the evolution of knowledge states more accurately, facilitating targeted educational interventions. The results of prediction on two publicly available datasets demonstrate that our proposed OPKT model achieves at least a 2% improvement in AUC, accurately assessing students’ learning outcomes and knowledge proficiency. Additionally, we conducted dimensionality reduction experiments and ablation studies to validate the interpretability of the model.

Quintuple-based Representation Learning for Bipartite Heterogeneous Networks

Recent years have seen rapid progress in network representation learning, which removes the need for burdensome feature engineering and facilitates downstream network-based tasks. In reality, networks often exhibit heterogeneity, which means there may exist multiple types of nodes and interactions. Heterogeneous networks raise new challenges to representation learning, as the awareness of node and edge types is required. In this article, we study a basic building block of general heterogeneous networks, the heterogeneous networks with two types of nodes. Many problems can be solved by decomposing general heterogeneous networks into multiple bipartite ones. Recently, to overcome the demerits of non-metric measures used in the embedding space, metric learning-based approaches have been leveraged to tackle heterogeneous network representation learning. These approaches first generate triplets of samples, in which an anchor node, a positive counterpart, and a negative one co-exist, and then try to pull closer positive samples and push away negative ones. However, when dealing with heterogeneous networks, even the simplest two-typed ones, triplets cannot simultaneously involve both positive and negative samples from different parts of networks. To address this incompatibility of triplet-based metric learning, in this article, we propose a novel quintuple-based method for learning node representations in bipartite heterogeneous networks. Specifically, we generate quintuples that contain positive and negative samples from two different parts of networks. And we formulate two learning objectives that accommodate quintuple-based learning samples, a proximity-based loss that models the relations in quintuples by sigmoid probabilities and an angular loss that more robustly maintains similarity structures. In addition, we also parameterize feature learning by using one-dimensional convolution operators around nodes’ neighborhoods. Compared with eight methods, extensive experiments on two downstream tasks manifest the effectiveness of our approach.

Nature of science and domain-specificity: investigating the coverage of nature of science in physics, chemistry and biology curricula across grade levels

ABSTRACT The paper investigates the coverage of nature of science (NOS) in different science subject curricula to understand how they emphasise various aspects of NOS across grade levels in secondary science education. Although various science curriculum analyses exist in the literature, there is a paucity of investigations that have combined the elements of domain-specificity and learning progression in relation to NOS curriculum content. Furthermore, a holistic and inclusive account of NOS is used to examine the cognitive, epistemic, social and institutional aspects of NOS. Physics, chemistry and biology high school curricula from Turkey are investigated. Each subject curriculum is analysed and compared using an analytical framework guided by the Family Resemblance Approach. Mixed methods approaches are used to summarise the quantitative and qualitative results. Findings suggest an overemphasis on the cognitive-epistemic aspects of NOS, considerable variation across science subject areas and non-uniform progression of NOS themes across grade levels. Analysis of curricula incorporating the domain-specific and learning progression elements of NOS is likely to enhance understanding of how to design robust curricula in different science subjects and to improve the teaching and learning of NOS. Implications for curriculum reform are discussed.

Cross‐attention neural network for land cover change detection with remote sensing images

AbstractLand cover change detection (LCCD) with remote sensing images (RSIs) is important for observing the land cover change of the Earth's surface. Considering the insufficient performance of the traditional self‐attention mechanism used in a neural network to smoothen the noise of LCCD with RSIs, in this study a novel cross‐attention neural network (CANN) was proposed for improving the performance of LCCD with RSIs. In the proposed CANN, a cross‐attention mechanism was achieved by employing another temporal image to enhance attention performance and improve detection accuracies. First, a feature difference module was embedded in the backbone of the proposed CANN to generate a change magnitude image and guide the learning progress. A self‐attention module based on the cross‐attention mechanism was then proposed and embedded in the encoder of the proposed network to make the network pay attention to the changed area. Finally, the encoded features were decoded to obtain binary change detection with the ArgMax function. Compared with five methods, the experimental results based on six pairs of real RSIs well demonstrated the feasibility and superiority of the proposed network for achieving LCCD with RSIs. For example, the improvement for overall accuracy for the six pairs of real RSIs improved by our proposed approach is about 0.72–2.56%.

A Study of Learning Progression in Science of Class IX Students of Balangir District, Odisha

A Study of Learning Progression in Science of Class IX Students of Balangir District, Odisha

“Learning from Each Other”: A Peer Education as an Alternative Teaching Option

This article presents a model of teaching as a new entry in the relevant bibliography, under the name “Learning from Each Other” (LEO) Teaching Model. The main purpose of LEO is the cognitive, emotional and social interaction and cooperation between learners of different ages and genders along with the mutual transmission of individual opinions, knowledge, attitudes, experiences, abilities and skills of learners, in the context of simultaneously taking on the role of the “tutor” and the “learner” as well. At the same time, the role of the teacher during the teaching is being revised by becoming not only a “teacher” but also a “learner.” Thus, for the teacher there are options about new researches and reflections regarding teaching as well as the progress and value of learning.

Simulation of E-learning in English personalized learning recommendation system based on Markov chain algorithm and adaptive learning algorithm

The research topic of this article is the English personalized learning recommendation module based on Markov chain algorithm and adaptive learning algorithm. Personalized learning recommendation systems have been widely applied in the field of education. Firstly, this article uses Markov chain algorithm to analyze user learning behavior data, abstract user learning behavior as a process of state transition, predict their future behavior based on their past behavior, better understand their learning needs and interests, and provide more personalized learning recommendations for them. Subsequently, combined with adaptive learning algorithms, recommendation strategies and content are dynamically adjusted based on the user's learning goals and level, providing more accurate and effective learning recommendations according to their personalized needs and learning progress. By comparing with traditional recommendation systems, evaluate the accuracy and personalization of the recommendation module to better meet the learning needs of users.

Control of Qubit Dynamics Using Reinforcement Learning

The progress in machine learning during the last decade has had a considerable impact on many areas of science and technology, including quantum technology. This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in a single qubit. The goal is to create an RL agent that learns an optimal policy and thus discovers optimal pulses to control the qubit. The most crucial step is to mathematically formulate the problem of interest as a Markov decision process (MDP). This enables the use of RL algorithms to solve the quantum control problem. Deep learning and the use of deep neural networks provide the freedom to employ continuous action and state spaces, offering the expressivity and generalization of the process. This flexibility helps to formulate the quantum state transfer problem as an MDP in several different ways. All the developed methodologies are applied to the fundamental problem of population inversion in a qubit. In most cases, the derived optimal pulses achieve fidelity equal to or higher than 0.9999, as required by quantum computing applications. The present methods can be easily extended to quantum systems with more energy levels and may be used for the efficient control of collections of qubits and to counteract the effect of noise, which are important topics for quantum sensing applications.

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named “CTPlantNet”. This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8.

Structures of neural network effective theories

We develop a diagrammatic approach to effective field theories (EFTs) corresponding to deep neural networks at initialization, which dramatically simplifies computations of finite-width corrections to neuron statistics. The structures of EFT calculations make it transparent that a single condition governs criticality of all connected correlators of neuron preactivations. Understanding of such EFTs may facilitate progress in both deep learning and field theory simulations. Published by the American Physical Society 2024

Integrating Artificial Intelligence (AI) Into Adult Education

This conceptual article provides a comprehensive overview of the current status of Artificial Intelligence (AI) integration and its influence on adult education. It discusses generative AI technologies and their potential applications in adult education settings, examines the opportunities and ethical challenges associated with integrating AI, and provides insights into emerging trends. The article consists of five sections. The introduction provides a rationale as to why AI should be integrated into adult education. Second, it describes evolving AI technologies such as Large Language Models (LLM) for personalized learning, Machine Learning Algorithms for adaptive learning systems, Virtual Reality (VR) and Augmented Reality (AR) for immersive learning experiences, Chatbots and virtual assistants for learner support and guidance, and Data Learning Analytics (DLA) for tracking learner progress and performance into adult education. Section three explores the ethical implications of AI in adult education, including academic honesty and integrity, data privacy, and algorithmic bias. In section four, emerging trends and future directions are discussed. The final section considers policy implications and makes recommendations for adult educators working to develop AI-enriched adult education.

Modeling and simulation of a double DQN algorithm for dynamic obstacle avoidance in autonomous vehicle navigation

Developing an autonomous vehicle in safe navigated open environments through self-learning is a formidable challenge. So, in this paper, an autonomous navigation implementation for urban environments with dynamic obstacles using a double DQN algorithm is proposed. Furthermore, the vehicle is trained in varied training environments, explored neural network architectures, and fine-tuned action and reward functions to optimize its performance. In conclusion, the vehicle performance is steadily improved with each iteration of training, as evident from the upward trend in the average reward graph. These graphs depict the vehicle's learning progress in the scenario where it is trained to avoid dynamic obstacles. Finally, the proposed algorithm shows better performance relative to the conventional algorithms. The entire work is modelled and simulated in CARLA software.

Learning outcome evaluation in manual assembly

Mass customization and shorter product life cycles are causing ever more variants in production, especially in manual assembly. At the same time, more diverse personnel structures are emerging due to demographic change and labor shortages. This is causing different challenges to production managers, e.g., competence gaps. To meet these challenges, learning in manual assembly becomes increasingly important. The design of the learning process can only be improved by checking whether the processes fulfill their purpose. Various learning evaluation measures are described in general vocational education and competence development, but it is hard to select the right one for the learning process. This paper shows a procedure, how learning evaluation measures can be selected, and how they can measure learning progress. For this, a test person study was conducted to compare different learning evaluation measures and show their usability and advantages in manual assembly. The results support making learning in assembly easier to apply and controllable. In the long term, feeding back the results improves the learning process design.

Connecting the threads: the role of multiplicative thinking in algebraic, geometrical, and statistical reasoning

ABSTRACT Making connections within and between different aspects of mathematics is recognised as fundamental to learning mathematics with understanding. However, exactly what these connections are and how they serve the goal of learning mathematics is rarely made explicit in curriculum documents with the result that mathematics tends to be presented as a set of discrete, disconnected topics. Interest in establishing a more coherent approach to the teaching and learning of school mathematics has led to a focus on big ideas. That is, networks of related concepts, skills and ways of thinking that facilitate learning mathematics with understanding. Research on learning progressions has helped identify what these big ideas are and how they serve to build connections within and between different aspects of mathematics. This paper draws on research that provides an evidenced-based learning progression for multiplicative reasoning to illustrate the connective role of multiplicative thinking in the development of algebraic, geometrical, and statistical reasoning.