Interactive Learning System Research Articles

Looping In: Exploring Feedback Strategies to Motivate Human Engagement in Interactive Machine Learning

This study investigates effective feedback mechanisms to maintain human engagement in interactive machine learning (IML) systems, focusing on social media platforms. We developed “Loop,” an IML system based on human-in-the-loop (HITL) principles that recommends content while encouraging users to report inaccuracies for model refinement. Loop implements three types of artificial intelligence (AI) feedback on user reports: (a) machine learning (ML)-centric, (b) personal-centric, and (c) community-centric feedback. In addition, we evaluated the relative effectiveness of these feedback types under two different task criticality scenarios: high and low. A user study with 30 participants was conducted to evaluate Loop through questionnaires and interviews. Results showed that participants preferred algorithmic improvements for personal benefit over altruistic contributions to the community, especially for low-criticality tasks. Furthermore, personal-centric feedback had a significant impact on user engagement and satisfaction. Our findings provide insights into the effectiveness of machine feedback in HITL-ML systems, contributing to the design of more engaging and effective IML interfaces. We discuss implications and strategies for encouraging proactive user engagement in HITL-ML-based systems, emphasizing the importance of tailored feedback mechanisms.

Deep transformer-based architecture for the recognition of mathematical equations from real-world math problems

Identifying mathematical equations from real-world math problems presents a unique and challenging task within the field of Natural Language Processing (NLP). It has a wide range of applications in various areas, such as academics, digital content design, and the development of automatic or interactive learning systems. However, the accurate understanding of these equations still needs to be enhanced due to the intrinsic complexity of mathematical symbols and various structural formats. Additionally, the unique syntax, diverse symbols, and complex structure of mathematical equations present significant obstacles that traditional NLP methods and Optical Character Recognition (OCR) systems need help to overcome. In this research, we utilize deep transformer architecture to recognize mathematical equations, and we have utilized our novel dataset with 3433 distinct observations. This dataset, which we have collected to include a diverse range of mathematical equations, is used to predict six (z=x+y, z=x−y, z=xy, z=x/y, y=x! and y=x) basic mathematical equations. We applied different transformer-based architectures, such as BERT, ELECTRA, XLNet, RoBERTa, and DistilBERT, and BERT performs best with 99.80% accuracy. To the best of our knowledge, this is the first NLP work in any language where we recognize the equation from the mathematical text.

Interactive Surgical Training in Neuroendoscopy: Real-Time Anatomical Feature Localization Using Natural Language Expressions.

This study addresses challenges in surgical education, particularly in neuroendoscopy, where the demand for optimized workflow conflicts with the need for trainees' active participation in surgeries. To overcome these challenges, we propose a framework that accurately identifies anatomical structures within images guided by language descriptions, facilitating authentic and interactive learning experiences in neuroendoscopy. Utilizing the encoder-decoder architecture of a conventional transformer, our framework processes multimodal inputs (images and language descriptions) to identify and localize features in neuroendoscopic images. We curate a dataset from recorded endoscopic third ventriculostomy (ETV) procedures for training and evaluation. Utilizing evaluation metrics, including "R@n," "IoU= θ," "mIoU," and top-1 accuracy, we systematically benchmark our framework against state-of-the-art methodologies. The framework demonstrates excellent generalization, surpassing the compared methods with 93.67 % accuracy and 76.08 % mIoU on unseen data. It also exhibits better computational speed compared with other methods. Qualitative results affirms the framework's effectiveness in precise localization of referred anatomical features within neuroendoscopic images. The framework's adeptness at localizing anatomical features using language descriptions positions it as a valuable tool for integration into future interactive clinical learning systems, enhancing surgical training in neuroendoscopy. The exemplary performance reinforces the framework's potential in enhancing surgical education, leading to improved skills and outcomes for trainees in neuroendoscopy.

Exploring a Mobile Technology-Driven Model for Intercultural Communication Education

In the context of accelerating globalization, intercultural communication competence has become a crucial element for the success of individuals and organizations. The frequent international interactions have underscored the importance of enhancing methods for improving intercultural communication skills within the educational sector. The rapid advancement of mobile technology offers unprecedented opportunities for educational innovation. Its convenience and widespread use have made mobile device-based learning models highly attractive. Particularly, advancements in real-time speech processing technologies have provided new tools and methods for intercultural communication education. By leveraging mobile real-time speech detection and synthesis technologies, a more interactive and personalized learning experience can be achieved, thereby enhancing the efficiency and effectiveness of language learning. This study aims to explore a mobile technology-based model for intercultural communication education and is divided into three main parts: firstly, the investigation of mobile real-time speech detection technologies aimed at intercultural communication education to provide instant feedback and improvement suggestions; secondly, the exploration of mobile real-time speech synthesis technologies to generate high-quality speech samples for learners to practice against; and thirdly, the integration of the aforementioned technologies to develop a flexible, efficient, and highly interactive learning system based on mobile technology. This study is expected to not only improve the effectiveness of intercultural communication education but also provide significant references for the innovative application of educational technologies.

Interactive multi-agent convolutional broad learning system for EEG emotion recognition

Electroencephalogram (EEG) emotion recognition is gaining significance in intelligent human–computer interaction. Multi-agent learning can capture more complete and reliable features, compensating for the lack of a single agent’s knowledge. However, previous EEG emotion recognition only focused on a single agent. Therefore, to extract effective features from high-dimensional EEG data, a novel interactive multi-agent (IMA) learning framework is proposed, and introduced into convolutional broad learning system (CNNBLS), then the interactive multi-agent convolutional broad learning system (IMA-CNNBLS) is proposed for EEG emotion recognition. It can effectively model high-dimensional EEG data, automatically extract EEG features related to emotions through convolutional neural network (CNN), and then extend the above features to a vast space to quickly extract generalized features through broad learning system (BLS), consider a CNNBLS as an agent, and multi-agent interact in the IMA part. As the theoretical basis of IMA-CNNBLS, the consistency is mathematically proved through the stationary distribution theory of Markov processes. To demonstrate the superiority of our proposed model, sufficient experiments are conducted on the DEAP, DREAMER and SEED datasets. The experimental results show the model can effectively improve the EEG emotion recognition accuracy, the best recognition performance is achieved on all datasets. In addition, our proposed model also shows the multi-agent interaction significantly affects EEG emotion recognition.

Eliciting Model Steering Interactions From Users via Data and Visual Design Probes.

Visual and interactive machine learning systems (IML) are becoming ubiquitous as they empower individuals with varied machine learning expertise to analyze data. However, it remains complex to align interactions with visual marks to a user's intent for steering machine learning models. We explore using data and visual design probes to elicit users' desired interactions to steer ML models via visual encodings within IML interfaces. We conducted an elicitation study with 20 data analysts with varying expertise in ML. We summarize our findings as pairs of target-interaction, which we compare to prior systems to assess the utility of the probes. We additionally surfaced insights about factors influencing how and why participants chose to interact with visual encodings, including refraining from interacting. Finally, we reflect on the value of gathering such formative empirical evidence via data and visual design probes ahead of developing IML prototypes.

EFFECTIVENESS OF USING INFORMATION TECHNOLOGY IN LEARNING CASE STUDY OF INTERACTIVE LEARNING SYSTEM IN CLASS A EDUCATIONAL TECHNOLOGY

This study evaluates the effectiveness of information technology in interactive learning among college students. Data was obtained through the questionnaire, showing that information technology in interactive learning is very effective. Most students responded positively to accessibility, flexibility, interactivity, engagement, self-support, and variety of learning materials. Expressly, 100% of students agreed with the questions about these benefits. However, there were differences of opinion on several aspects. Regarding the suitability of learning speed and style, only 72.7 % of students felt that this system was suitable for their needs, while 27.3% thought it needed more. In addition, 90.9 % of students stated the ease of use and effectiveness of interaction, but some were still dissatisfied with these aspects. The conclusion of this study shows that although most students feel positive benefits from using information technology in learning, several areas need improvement, such as adjusting the speed and style of knowledge and the system's ease of use. Recommendations from this study include increasing system flexibility, optimizing interface design, and providing additional support for students. These findings can be a reference for developers of interactive learning systems and educational institutions who want to integrate information technology into the learning process more effectively.

Decomposed Inductive Procedure Learning: Learning Academic Tasks with Human-Like Data Efficiency

Human brains have many differently functioning regions which play specialized roles in learning. By contrast, methods for training artificial neural networks, such as reinforcement-learning, typically learn exclusively via a single mechanism: gradient descent. This raises the question: might human learners’ advantage in learning efficiency over deep-learning be attributed to the interplay between multiple specialized mechanisms of learning? In this work we review a series of simulated learner systems which have been built with the aim of modeling human student’s inductive learning as they practice STEM procedural tasks. By comparison to modern deep-learning based methods which train on thousands to millions of examples to acquire passing performance capabilities, these simulated learners match human performance curves---achieving passing levels of performance within about a dozen practice opportunities. We investigate this impressive learning efficiency via an ablation analysis. Beginning with end-to-end reinforcement learning (1-mechanism), we decompose learning systems incrementally to construct the 3-mechanism inductive learning characteristic of prior simulated learners such as Sierra, SimStudent and the Apprentice Learner Architecture. Our analysis shows that learning decomposition plays a significant role in achieving data-efficient learning on par with human learners---a greater role even than simple distinctions between symbolic/subsymbolic learning. Finally we highlight how this breakdown in learning mechanisms can flexibly incorporate diverse forms of natural language and interface grounded instruction, and discuss opportunities for using these flexible learning capabilities in interactive task learning systems that learn directly from a user’s natural instruction.

INOVASI PEMBERDAYAAN PAUDQU DARUSSOLIHIN: MEMBANGUN PEMBELAJARAN AL-QURAN INTERAKTIF, MEMPERKENALKAN BAHASA ARAB DAN MENINGKATKAN LITERASI PERBANKAN SYARIAH

Social Work Lecture (KKS) is an activity that involves students to be able to participate directly in community activities. After getting lecture material in the form of theory, this KKS is a form of real community service to apply the knowledge gained during lectures. This activity was carried out by FAI UHAMKA students class of 2020. The target of this service is children and the surrounding community in PaudQU Darussolihin. PaudQU Darussolihin, as an institution that has a vision and mission to improve the understanding of Islam for children and the community, especially in reading the Qur'an, is considered a strategic partner to achieve this goal. Students realize that there are a number of challenges faced by the community, especially related to understanding the Quran, Arabic language skills, and Islamic banking literacy. Therefore, students of the Faculty of Islamic Religion, Prof. Dr. Hamka University plan a PSC Program that will focus on innovation in empowering. Through this program, it is expected to build an interactive Quran learning system, utilizing technology to increase the attractiveness and effectiveness of learning. In addition, this activity will also introduce Arabic more thoroughly, from basic to advanced, so that children and our society The surrounding community can understand and apply the language in everyday life. No less important, this program will also provide a deep understanding of Islamic banking literacy, in the form of seminars, and provide practical skills in managing Islamic finances.

Мультилингвальная интерактивная обучающая система для подготовки военнослужащих стран ОДКБ

The article delves into the interactive possibilities of innovative learning technologies that allow to ensure the formation of communicative competence of foreign cadets of military universities of the Russian Federation. In addition, the work gives the justification of multilingual representation of learning systems and outlines the op-tions for their application in preparation for lectures and laboratory works. The research describes the levels of interactivity and key characteristics of innovative learning technologies that ensure the realization of the Con-cept of development of electronic information and educational environment of military higher education institu-tion, approved in 2020. The fragment of the multilingual interactive learning system presented in the article is designed for the military personnel of the CSTO countries and can be implemented in the process of conduct-ing joint classes with this category of people. Such a system can serve as a component of the electronic infor-mation and educational environment and be used in the process of independent training of foreign military personnel.

Application of Artificial Intelligence and Speech Data System based on Music Internet Course Learning System

Abstract Music education, as an important part of art education, can also take advantage of artificial intelligence technology to achieve more efficient personalized teaching. The direction of the application of artificial intelligence technology in music course learning is explored in this paper. Therefore, a music data technology interactive learning system is proposed. The music recognition module’s design utilizes a deep neural network model to model the complex problem of speech signal recognition. The encoder formula is obtained by representing the implicit layer feature vectors in the sample through a mathematical model. After encoding and decoding, as well as designing the activation function, the HMM algorithm is introduced to realize the application of DNN-HMM in acoustic modeling. Using a digital filter, the spectrum of the speech signal is smoothed, and the spectrogram is obtained by Fourier variation to visualize the representation of the speech frequency domain. The design of a music Internet teaching course is based on the method proposed in this paper. The melody recognition accuracy of the system is tested through simulation experiments, in which the distribution of auditory feature points of the piano ranges from 0.66 to 0.69. The distribution of rock music is above 0.7, and there is no overlap between the two audio datasets, which indicates that the system proposed in this paper has good recognition accuracy of audio features. Using the speech analysis module, the students’ music learning performance is analyzed. After the model designed in this paper to assist music learning, students’ music performance mean value is 4.397, and the control group’s performance is 3.565. The difference is 0.832. The system designed in this paper is more effective for music learning.

Construction of Online Interactive Learning System for University English Driven by Artificial Intelligence

Abstract With the advent of rapid advancements in information technology, online learning has increasingly gained traction, prompting a multitude of colleges and universities to adopt innovative reforms in their educational delivery methods. This research undertakes the development of a college English online interactive learning system empowered by artificial intelligence. It primarily investigates the interactive behaviors of college students in online English learning environments. Initially, the study employs the K-means algorithm to categorize students based on their distinct learning behaviors. Subsequently, it leverages a recommendation algorithm to fine-tune the provision of English teaching resources online. Furthermore, the study integrates an enhanced Apriori algorithm to extract associations between student behaviors and learning outcomes. This analytical framework underpins the empirical evaluation of the system’s efficacy in actual educational settings, aiming to deepen the understanding of students’ online interactive behaviors. The findings reveal that over 95% of students consistently log into the learning platform on scheduled class days. The metrics for course recommendation success, such as the hit rate, the average inverse rank, and the range of normalized discounted cumulative gain, are reported between 18.37-21.94, 1.87-2.08, and 4.44-4.87, respectively. Additionally, system memory utilization remains stable at 57%-66%, corroborating the system’s operational effectiveness. This research contributes valuable insights and benchmarks for the development of robust online interactive learning systems.

Design Numbers-Recognizing Game with Interactive Projection for Young Children

Early Childhood Education (PAUD) requires an interactive and attractive learning system. The PAUD learning method is also capable of stimulating the knowledge development of the students, especially in the cognitive aspect. Such an aspect enters into an educational game where there is an element of education obtained from something that exists and is inherent as well as being part of the game itself. The activities that have these educational elements can stimulate and train the development of the child's brain and stimulate the creativity of children's thinking. The use of technology has a strong influence on the development of education in children of PAUD age. Interactive Projection is one of the learning media for students to understand numbers easily and fun. Target kids are there at the age of three to five, with introductions starting from physical numbers, how to write numbers, counting some objects and matching numbers. Numberpedia uses blackbox method as a testing method. From the blackbox method, some several questions were carried out to support functional testing, a percentage value of 90% was obtained.

Driver Maneuver Interaction Identification with Anomaly-Aware Federated Learning on Heterogeneous Feature Representations

Driver maneuver interaction learning (DMIL) refers to the classification task with the goal of identifying different driver-vehicle maneuver interactions (e.g., left/right turns). Existing conventional studies largely focused on the centralized collection of sensor data from the drivers' smartphones (say, inertial measurement units or IMUs, like accelerometer and gyroscope). Such a centralized mechanism might be precluded by data regulatory constraints. Furthermore, how to enable an adaptive and accurate DMIL framework remains challenging due to (i) complexity in heterogeneous driver maneuver patterns, and (ii) impacts of anomalous driver maneuvers due to, for instance, aggressive driving styles and behaviors. To overcome the above challenges, we propose AF-DMIL, an Anomaly-aware Federated Driver Maneuver Interaction Learning system. We focus on the real-world IMU sensor datasets (e.g., collected by smartphones) for our pilot case study. In particular, we have designed three heterogeneous representations for AF-DMIL regarding spectral, time series, and statistical features that are derived from the IMU sensor readings. We have designed a novel heterogeneous representation attention network (HetRANet) based on spectral channel attention, temporal sequence attention, and statistical feature learning mechanisms, jointly capturing and identifying the complex patterns within driver maneuver behaviors. Furthermore, we have designed a densely-connected convolutional neural network in HetRANet to enable the complex feature extraction and enhance the computational efficiency of HetRANet. In addition, we have designed within AF-DMIL a novel anomaly-aware federated learning approach for decentralized DMIL in response to anomalous maneuver data. To ease extraction of the maneuver patterns and evaluation of their mutual differences, we have designed an embedding projection network that projects the high-dimensional driver maneuver features into low-dimensional space, and further derives the exemplars that represent the driver maneuver patterns for mutual comparison. Then, AF-DMIL further leverages the mutual differences of the exemplars to identify those that exhibit anomalous patterns and deviate from others, and mitigates their impacts upon the federated DMIL. We have conducted extensive driver data analytics and experimental studies on three real-world datasets (one is harvested on our own) to evaluate the prototype of AF-DMIL, demonstrating AF-DMIL's accuracy and effectiveness compared to the state-of-the-art DMIL baselines (on average by more than 13% improvement in terms of DMIL accuracy), as well as fewer communication rounds (on average 29.20% fewer than existing distributed learning mechanisms).

Designing a Smart Classroom Based on a Multi-Screen Interactive Learning System for Improving Student Satisfaction

There are several issues with traditional classroom teaching, including the sporadic presentation of lecture materials, the reliance on a single, traditional teaching method, and a lack of student engagement and initiative. These problems seriously impact the quality of engineering education. Combining the advantages of mobile learning and multi-screen displays, this paper proposes a smart classroom architecture based on a multi-screen interactive learning system (MSILS). The proposed smart classroom system supports students in conducting learning activities in a multi-screen learning environment. Its goal is to enhance interaction and increase student satisfaction in engineering classroom learning. For the evaluation, an experiment was conducted in a science and engineering-related course at a university in China. The research results show that students who received instruction with the proposed system reported higher satisfaction in four areas: information technology, teaching methods, perceived value, and learning satisfaction. Approximately 90% of the 629 undergraduate students who participated in the experiment expressed their desire to continue their studies in this smart classroom. This indicates that students are more interested, expectant, helpful, and engaged in the target courses they are taking after using the smart classroom system.

ML Pro: digital assistance system for interactive machine learning in production

The application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution for industrial use cases requires multi-layered, sophisticated decision-making processes along the pipeline that can only be accomplished by subject matter experts with knowledge of statistical mathematics, coding, and engineering process knowledge. By having humans and computers work together in a digital assistance system, the special characteristics of human and artificial intelligence can be used synergistically. This paper presents the development of a digital human-centered assistance system for employees in the production and development departments of industrial manufacturing companies. This assistance system enables users to apply production-specific data mining and machine learning techniques without programming to typical tabular production data, which is often inherently high-dimensional, nonstationary, and highly imbalanced data streams. Through tight interactive process guidance that considers the dependencies between machine learning process modules, users are empowered to build and optimize predictive models. Compared to existing commercial and academic tools with similar objectives, the digital assistance system offers the added value that both classical shallow and deep learning as well as generative and oversampling methods can be interactively applied to all feature table use cases for different user modes without programming.

ANDROID-BASED DEVELOPMENT OF INTERACTIVE MULTIMEDIA LEARNING APPLICATION SYSTEMS FOR CHILDREN

<em>Technological developments have progressed very rapidly, marked by progress in the field of technology and information. Information technology has now spread to almost all aspects of life, one of which is that it has entered the educational aspect which is used as an interesting learning medium, because it is able to provide new colors in the learning process, and can use a learning while playing approach so that the learning process can feel happy, so that later students are able to express themselves maximally in the learning process. In education there are many things that need to be considered in child development so as to avoid various obstacles in the learning process, such as color blindness, speech delays, and other pre-school abilities by introducing cognitive education to children such as recognition of numbers, letters, colors. And according to IDAI this cognitive recognition period is recommended for children aged 2 -5 years. Researchers developed an Android-based interactive multimedia learning system for children with the aim of providing facilities to parents for children in introducing cognitive aspects with new colors, namely in the form of interactive multimedia applications. The development method used is MDLC (Multimedia Development Life Cycle), because the application being developed involves various media such as sound, images and text. The development of this application also applies quiz content to maximize children's learning media. Through testing using Black Box Testing, the WARUKA Education application that was developed managed to achieve good results. All the features contained in the application run well and as expected. In the User Acceptance Testing test using a questionnaire, the WARUKA Education application gets positive results, with a user satisfaction percentage of 94.167%. Based on this percentage, the WARUKA Education application can be categorized as very good, indicating very high acceptability for users.</em>

Retracted: Design and Construction of an Interactive Intelligent Learning System for English Learners in Higher Education Institutions

Retracted: Design and Construction of an Interactive Intelligent Learning System for English Learners in Higher Education Institutions

Application of Mobile-Based Augmented Reality Technology Concepts in Interactive Learning Systems for Liquefaction Phenomena for the Deaf

One of the natural disasters currently happening in Indonesia is an earthquake. An earthquake is a condition where the earth shakes due to volcanic activity and collisions due to the movement of the earth's plates. Earthquake activity causes many problems. One of them is liquefaction. Liquefaction is an event that indicates a loss of soil shear strength due to an increase in pore water pressure. This occurs because the earthquake load occurs so quickly and briefly. Liquefaction is an illustration of the effect of an earthquake so that the soil layer loses its strength. The public rarely knows the phenomenon of earthquakes and liquefaction. Not many people know how the process of the movement of the earth is the forerunner of earthquakes, especially people with hearing impairments. They could only feel an earthquake. Therefore, there is a need for education that can be conveyed to the public. Educational techniques in the virtual form will be a special attraction for people, especially people who are deaf or hard of hearing. Augmented Reality (AR) technology is a technology where virtual objects and real objects are combined. The AR is used to create virtual and interactive learning media. This study produced an application as a learning media that utilizes AR technology with the Marker Based Tracking method. The technology was developed using the MDLC (Multimedia Development Life Cycle) method for the learning media to be more interactive. The results of the tests concluded that the application could be used as an interactive learning medium to increase knowledge about earthquakes and the effects of liquefaction on the community.

Effectiveness of an extended-reality interactive learning system in a dance training course

Effectiveness of an extended-reality interactive learning system in a dance training course