Personalized Instruction Research Articles

A New Method Based on Belief Rule Base with Balanced Accuracy and Interpretability for Student Achievement Prediction

In the field of education, the accurate prediction of students’ future performance is essential for personalized instruction and efficient allocation of resources. Such predictions not only help education professionals develop targeted educational strategies but also identify students’ learning needs at an early stage so that timely interventions and support can be provided. To gain the trust of educational experts and ensure the practical application value of the prediction results, the prediction methods used must be highly interpretable. However, there are two problems with the current belief rule base (BRB) applied to student performance prediction. First, there is a current lack of effective strategies for enhancing the interpretability of the optimization process. Second, BRB models that overemphasize accuracy tend to exhibit characteristics of black-box models. To overcome these challenges, this paper proposes a new method based on BRB with balanced accuracy and interpretability (BRB-Bai) for student achievement prediction. First, an attribute selection method is proposed to filter out important features associated with student performance. Then, expert knowledge credibility is calculated, and four interpretability strategies are proposed to ensure the interpretability of the model and to achieve a balance between interpretability and accuracy on the basis of expert knowledge credibility. The effectiveness of the proposed model is demonstrated by conducting experiments on the student achievement dataset.

Equity in Literacy Education: A Case Study of Tier 2 Personalized Instruction

According to the NAEP, proficiency in reading is reported to be 67% in 4th-grade students. General education classroom teachers are responsible for providing Tier 1 and 2 instruction, including interventions in Tier 2. However, many teachers are unsure about implementing interventions and have had negative experiences with Response to Intervention (RTI), finding the process frustrating and overwhelming. This case study examines the effectiveness of teachers who have seen an increase in reading levels and student interest. The research explores the teachers’ perceptions, experiences, and skills of the current implementation of RTI using Gibbs’ Reflective Practice as a theoretical framework. Data collected from individual interviews and observations are analyzed using open coding. Themes and concepts are identified from the interview and observational data. These findings inform educators and higher education teacher educators about Tier 2 implementation and equitable, personalized instruction in the RTI process, aiming to strengthen the implementation and effectiveness of Tier 2 interventions in the general education classroom.

Effectiveness of an Educational Seminar for Older Adults With Hearing Loss

Purpose: Despite the recognized benefits of aural rehabilitation (AR), its adoption has been limited. The purpose of this study was to investigate the effectiveness of group AR through an educational seminar for older adults with hearing loss, “Optimal Hearing,” evaluating the application of various patient-reported outcome measures. Method: Participants were older adults with hearing loss. The seminar covered content such as hearing loss and aging, audiogram interpretation, clear speech, lipreading, and hearing aids. Small group activities for personalized instruction also included: the Client Oriented Scale of Improvement (COSI), Communication Confidence Profile (CCP), and, a study-specific measure, the Optimum Hearing Questionnaire (OHQ). Results: Participants experienced significant improvements in various areas measured by the COSI. The CCP showed significant change in one item (confidence while conversing with friends in a noisy restaurant), while several items on the OHQ indicated significant changes postintervention. Conclusion: The findings suggest the potential clinical utility of these measures in group AR settings and the importance of individualized goal setting. Supplemental Material: https://doi.org/10.23641/asha.26824111

Generative Artificial Intelligence and Large Language Models in Primary Care Medical Education.

Generative artificial intelligence and large language models are the continuation of a technological revolution in information processing that began with the invention of the transistor in 1947. These technologies, driven by transformer architectures for artificial neural networks, are poised to broadly influence society. It is already apparent that these technologies will be adapted to drive innovation in education. Medical education is a high-risk activity: Information that is incorrectly taught to a student may go unrecognized for years until a relevant clinical situation appears in which that error can lead to patient harm. In this article, I discuss the principal limitations to the use of generative artificial intelligence in medical education-hallucination, bias, cost, and security-and suggest some approaches to confronting these problems. Additionally, I identify the potential applications of generative artificial intelligence to medical education, including personalized instruction, simulation, feedback, evaluation, augmentation of qualitative research, and performance of critical assessment of the existing scientific literature.

Quantum computing enhanced knowledge tracing: Personalized KT research for mitigating data sparsity

With the development of artificial intelligence in education, knowledge tracing (KT) has become a current research hotspot and is the key to the success of personalized instruction. However, data sparsity remains a significant challenge in the KT domain. To address this challenge, this paper applies quantum computing (QC) technology to KT for the first time. It proposes two personalized KT models incorporating quantum mechanics (QM): quantum convolutional enhanced knowledge tracing (QCE-KT) and quantum variational enhanced knowledge tracing (QVE-KT). Through quantum superposition and entanglement properties, QCE-KT and QVE-KT effectively alleviate the data sparsity problem in the KT domain through quantum convolutional layers and variational quantum circuits, respectively, and significantly improve the quality of the representation and prediction accuracy of students’ knowledge states. Experiments on three datasets show that our models outperform ten benchmark models. On the most sparse dataset, QCE-KT and QVE-KT improve their performance by 16.44% and 14.78%, respectively, compared to DKT. Although QC is still in the developmental stage, this study reveals the great potential of QM in personalized KT, which provides new perspectives for solving personalized instruction problems and opens up new directions for applying QC in education.

Integrating Artificial Intelligence (AI) in Language Teaching: Effectiveness, Challenges, and Strategies

Integrating artificial intelligence (AI) technologies into various domains of education has emerged as a promising avenue for enhancing teaching and learning experiences. Language teaching, in particular, benefits significantly from AI’s capabilities, offering unprecedented opportunities to support educators in delivering more effective and personalized instruction to diverse student populations. The research employed a mixed-method approach using a quant-qual research design. to investigate AI’s integration into language teaching. Results from n=100 language teachers indicate that educators perceive AI to be effective across multiple aspects. Different AI systems have varying levels of perceived effectiveness but are nevertheless welcomed positively. From the profile of teachers using AI, to the kinds of AI used and functions, there are varying distinctions as well as the acceptance of AI integration in language instruction. The fact that AI is being widely used to improve language instruction through interactive simulations, adaptive learning, and individualized feedback is evidence of this. Despite challenges such as technological constraints and pedagogical alignment, educators have reported successes in improving student engagement, proficiency, and autonomy through the strategic use of AI. These insights underscore the importance of continued exploration and implementation of innovative strategies to harness the full potential of AI in language education.

Adaptive Learning Models for Diverse Classrooms: Enhancing Educational Equity

This review paper explores the transformative potential of adaptive learning models in promoting educational equity. Adaptive learning systems leverage advanced algorithms and digital tools to provide personalized instruction tailored to individual student needs, enhancing academic outcomes and fostering greater engagement and confidence. The theoretical foundation of adaptive learning aligns with constructivist and socio-cultural theories, emphasizing the importance of personalized and differentiated instruction. Evidence suggests adaptive learning improves academic performance among diverse and underserved student populations. However, significant barriers to implementation persist, including technological, financial, and pedagogical challenges, as well as ethical considerations related to data privacy and algorithmic fairness. The paper highlights the implications of these findings for educational policy and classroom practice, advocating for equitable access to adaptive learning technologies and comprehensive professional development for educators. The evolution of adaptive learning, driven by advancements in artificial intelligence and machine learning, promises further educational equity and inclusivity advancements. Keywords: Adaptive Learning, Educational Equity, Personalized Instruction, Digital Tools

Evaluation of a Video-Based Concept for Hand Hygiene Education of Parents in a Neonatal Intensive Care Unit.

Current clinical guidelines support family-centered care in Neonatal Intensive Care Units (NICUs). This implies parents should also be involved in the most critical patient safety measures. Hand hygiene is the single most important tool to prevent healthcare-associated infections and related long-term effects. Although often studied in healthcare workers, the hand hygiene compliance of parents is rarely assessed. The aim of this study was to evaluate the effectiveness of an educational video, available in ten different languages, in teaching parents hand hygiene techniques in a NICU, lowering the burden on the staff. Parents in the intervention group were educated through a video; the control group received personal instruction from healthcare workers. The primary outcome parameter was the predicted probability of passing a subsequent hand scan. The quality of hand hygiene among parents educated through the video was at least as good as that of those who received instruction from a healthcare worker, demonstrated by a higher predicted probability of passing the hand scan (43.8% vs. 57.1% in male and 67.9% vs. 75.9% in female participants). The feedback from the intervention group was predominantly positive, with most parents (62%) expressing a preference for video-based education. Implementing a video-based approach seems to be effective for educating parents about hand hygiene in a NICU and was well accepted by the parents. This method offers a consistent standard of hand hygiene education, helps to overcome language barriers, and can also be used as regular reminder of the importance and proper technique of hand hygiene.

Implementing Triage-Bot: Supporting the Current Practice for Triage Nurses.

In Canada, emergency departments (ED) have 15.1 million unscheduled visits every year; this has been suggested to indicate that patients rely on ED to address the gaps experienced by 6.5 million Canadians who lack a primary care provider. When this large number of visits is coupled with a predicted shortage of 100,000 nurses in Canada by 2030, ED can be expected to face resource limitations, which highlights the importance of triage systems as a source of immediate support. Technology that incorporates innovative analytical methods, automation of routine, and efficient processing can be leveraged to enhance patient outcomes, streamline clinical processes, and improve the overall quality and efficiency of healthcare delivery. This paper aims to highlight how the Triage-Bot, a proposed AI system, can assist ED nurses when triaging patients. The Triage-Bot system is based on the Canadian Triage and Acuity Scale (CTAS), which currently serves as a standardized and highly effective tool for prioritizing patient care in emergency departments across the country. Pre-set and open-ended questions are asked using voice and video, allowing patients to describe their health concerns and conditions. Triage-Bot automatically measures the following vital signs: heart rate (HR), heart rate variability (HRV), oxygen saturation (SpO2), respiratory rate (RR), blood pressure (BP), blood glucose (BG), and stress. The system uses artificial intelligence models, particularly those with a deep learning approach that simultaneously analyzes both the user's facial expression and voice tone. Implementation: A systematic review addressed the implications of AI in nursing and concluded that it could contribute to patient care by providing personalized instructions and/or remotely monitoring patients. The Triage-Bot system can be implemented in healthcare facilities, such as emergency department waiting rooms. The information it collects can then be added to a patient's health records to support nurses in assessing the severity of each patient's condition. Limitations: If the system is accessed without a nurse's guidance, it is imperative that the user receives information regarding when to visit a healthcare provider or ED. Continuous improvements in Triage-Bot's accessibility for patients with varying abilities are required to ensure that the system remains user-friendly during times of illness. The voice and text interaction can also be influenced by a user's understanding of language, culture, and age-related factors.

Differentiated Instructional Practices and Academic Performance in Mathematics

This study addresses the challenge of student performance in Mathematics by evaluating the effectiveness of differentiated instructional practices. Conducted in the Division of El Salvador City during the 2022-2023 school year, the research involved 156 teachers. It aimed to explore the respondents' profiles, the effectiveness of varied instructional methods, and their relationship to student performance in Mathematics during the First and Second Quarters. Data was collected through a descriptive questionnaire and analyzed using statistical tools like mean, frequency, standard deviation, and Pearson correlation. The findings indicate a significant positive relationship between differentiated instructional practices and students' academic performance in Mathematics. Differentiated instruction, which caters to diverse learning needs, enhances students’ understanding by fostering engagement, motivation, and comprehension. The study concludes that Mathematics teachers possess a moderate level of proficiency in using differentiated instructional practices. It highlights the importance of supportive learning environments, continuous professional development, and access to necessary resources for promoting academic success. Recommendations include ongoing professional development in Mathematics instruction and differentiation strategies, as well as targeted intervention programs for struggling students. Aligning tiered assignments with curriculum goals is also emphasized to ensure personalized instruction and the effectiveness of classroom tasks.

AI-Enabled E-Learning Systems: A Systematic Literature

Today e-learning system plays an essential role in the education system. Technology integration in teaching helps to teach content-based curriculum effectively and efficiently to build confidence among students. Personalized learning systems focus on learning behavior, interest, and design curriculum according to learners‟ ability and basic knowledge. It is a flexible teaching methodology to meet the individual needs of students. The personalized learning approach optimizes the needs of each learner. For an effective education system, it is necessary to understand learners and develop a plan that copes up with the individual learning needs and the interest of students‟. An intelligent Tutor system is an expert system to monitor the learners‟ performance to provide personalized coaching. E-learning applications include computer-based learning, web-based learning, digital collaboration and virtual classrooms. Artificial Intelligence can be used for automating learning activities like designing teaching tools, curriculum, training, evaluating students‟ performance, and using modern teaching methodology. Artificial intelligence is the most modern e-learning trend in higher education and the corporate world. AI helps to provide individual decisions using data analytics that leads to better education for personalized instruction to streamline the education process.

An Experiential Journey: A Year of a Professor Using AI in the Classroom and Research

This paper explores the integration of artificial intelligence (AI) in education and research, highlighting its potential to transform traditional teaching methods and enhance academic inquiry. The use of AI in education is examined, focusing on its ability to streamline administrative tasks, provide personalized instruction, and support teacher professional development. The benefits and challenges of AI adoption in the classroom are discussed, including its impact on teacher burnout and the potential disruption of teacher-student relationships. Additionally, the paper explores the applications of AI in scientific research, emphasizing its role in data analysis, literature review, predictive modeling, and collaborative research. The experiences of a professor who integrated AI into their classroom are shared, and the decision-making process, adjustments made, and the importance of balancing AI-driven instruction with interactive in-class activities are discussed. The paper highlights the need for ongoing evaluation, adaptation, and human-AI collaboration to ensure AI's responsible and effective integration in education and research.

Student’s Perception On The Use Flipped Classroom In Learning English at The Second Grade Of SMPN 2 X Koto

This research aimed to investigate the students’ perceptions of the flipped classroom approach in learning English at SMPN 2 X Koto Tanah Datar. The flipped classroom model, which reversed traditional in-class activities and homework, introduced new content at home through videos or readings, and dedicated class time to interactive, hands-on activities. This method sought to enhance student engagement, promote active learning, and provide opportunities for personalized instruction and immediate feedback. The study employed a descriptive quantitative method, focusing on second-grade students. Data were collected using a questionnaire that was distributed to 64 students, who were selected through purposive sampling. The questionnaire aimed to gauge students' perceptions of various aspects of the flipped classroom, including self-directed learning, collaboration, responsibility, learning performance, flexibility, and student satisfaction. The population for this study consisted of all second-grade students at SMPN 2 X Koto for the academic year 2021/2022, totaling 95 students across three classes. The sample was selected using purposive sampling, focusing on classes VIII.1 and VIII.2, to ensure participants had the specific attributes necessary for the research. Based on the findings and analysis, it can be concluded that students at SMPN 2 X Koto Tanah Datar have a highly favorable perception of the Flipped Classroom approach in learning English. The study, which involved 95 second-grade students, revealed positive responses across various aspects such as autonomy in learning, collaboration, responsibility, learning performance, flexibility, individualized experiences, interactions, and overall satisfaction. With an average score of 81.41%, categorized as "Very Good" by Arikunto's criteria, the Flipped Classroom methodology is well-received and deemed beneficial for English education at SMPN 2 X Koto Tanah Datar.

Evaluating Artificial Intelligence on the Efficacy of Preference Assessments for Preservice Speech-Language Pathologists

AbstractIndividuals with intellectual and developmental disabilities (IDD) face many barriers to meaningful inclusion, including limited language and communication skills. Professionals, such as speech-language pathologists (SLPs), can provide personalized instruction to promote skill development and inclusion. Providing opportunities for individuals to express preferences and choice, such as the multiple stimulus without replacement preference assessment (MSWO; DeLeon & Iwata 1996), within these programs, further increases skill acquisition and social interaction. However, limitations in professionals’ knowledge and skills in performing assessments can be another barrier to meaningful inclusion for individuals with IDD and traditional training methods can be challenging and time consuming. The purpose of the current study was to compare the use of artificial intelligence with traditional pen and paper self-instructional MSWO training methods for five preservice SLPs. Fidelity of implementation and duration of assessment were measured. Results demonstrated a large increase in implementation fidelity for two participants, a moderate increase for two participants and a slight increase for the remaining participant while using artificial intelligence. All participants demonstrated a decrease in scoring errors using artificial intelligence. Regarding duration of implementation, artificial intelligence resulted in a significant reduction for four participants and a moderate reduction for the remaining participant. Results of the follow-up survey suggest that all adult participants and both child participants found that artificial intelligence had a higher treatment acceptability and was more effective at producing socially significant outcomes than traditional methods. Recommendations for clinicians and future research are discussed.

Assessing the relationship between anxiety and the adoption of Artificial Intelligence tools among mathematics preservice teachers

Many revelations have been made about the revolution that artificial intelligence (AI) has brought to the education sector, including the opening of opportunities for personalised instruction, boosting the quality of content developed by teachers while preparing for lessons, and improving the quality of classroom evaluations. Despite the many benefits of AI adoption, there have been concerns and apprehensions about its use in the educational sector. A survey was conducted to investigate the relationship between AI anxiety and the adoption of artificial intelligence tools among mathematics preservice teachers who are university undergraduates studying mathematics education in Ekiti State, Nigeria. The study sample consisted of 129 mathematics preservice teachers selected through purposive sampling. The AI anxiety scale and AI adoption scale were used for data collection after being tested for reliability. The data collected through the scales were analysed using descriptive and inferential statistics. The findings of the study revealed that the mathematics preservice teachers had a high level of AI anxiety and adopted AI at a moderate level. The study further showed that there is a significant weak relationship between mathematics preservice teachers' AI-Anxiety and AI adoption. Also, there is no significant gender difference in mathematics preservice teachers' AI anxiety and AI adoption. Based on the findings of the study, it was recommended that teacher education programs include AI and digital literacy in the curriculum to prepare students for the seamless integration of AI. Additionally, targeted interventions should be implemented to reduce the anxiety exhibited by preservice teachers.

EFFECTS OF TEACHER COACHING SKILLS ON INQUIRY-BASED LEARNING IN SCHOOL ENVIRONMENT MANAGEMENT

The article argues that developing teacher coaching skills in managing the school environment plays an important role in promoting successful inquiry-based learning in the school environment. This approach encourages students to ask questions, explore topics in depth, and take an active role in their learning, leading to increased engagement and motivation. Students are encouraged to analyze information, make connections, and draw conclusions independently, helping them gain a deeper understanding of concepts. It helps adjust instructional strategies and interventions to meet specific learning needs in the school environment, leading to more personalized instruction. In essence, teacher coaching in inquiry-based learning acts as a catalyst for changing teaching practices and student learning experiences. It enables educators to create an engaging, student-centered environment where inquiry, exploration, and critical thinking thrive, ultimately benefiting the entire school community.

Optimization of English Classroom Interaction Models Incorporating Machine Learning

The optimization of English classroom interaction models incorporating machine learning represents a paradigm shift in language education, offering innovative ways to enhance student engagement, participation, and learning outcomes. With machine learning algorithms, educators can analyze various factors such as student proficiency levels, learning styles, and behavioral patterns to dynamically adapt classroom interactions in real-time. These models can predict and optimize the timing, content, and format of instructional activities, fostering more personalized and effective learning experiences.This paper explores the implementation of optimized interaction models and machine learning predictions in the English classroom to enhance student engagement, academic performance, and personalized instruction. Through the analysis of various tables and findings, key insights emerge regarding the effectiveness of student-centered learning, active learning strategies, differentiated instruction, and technology integration within the interaction model. Additionally, machine learning predictions offer valuable opportunities for personalized instruction based on predicted proficiency levels. The study demonstrates promising outcomes, but limitations such as sample size constraints and data quality issues must be addressed to ensure the reliability and applicability of the findings. Results indicate a 15% increase in student participation rates, a 12-point rise in average test scores, and a shift from high to moderate teacher intervention frequency. Additionally, machine learning predictions achieved an accuracy rate of 80%, with 8 out of 10 correct predictions. Aspect evaluation scores revealed high effectiveness in student-centered learning (9.2), formative assessment (9.3), and teacher facilitation (9.0). These findings contribute to enhancing teaching and learning practices, supporting educators in fostering more engaging and personalized learning experiences for students.

Student profiles based on in‐game performance and help‐seeking behaviours in an online mathematics game

AbstractWell‐designed online educational games can improve students' math knowledge, skills and engagement; however, more research is needed to understand how to formatively assess components of students' mathematical understanding and learning as students solve problems in online educational games. In this study, we examined how 7th‐grade students' (N = 760) in‐game performance (ie, strategy efficiency, mathematical validity of the first action, mathematical errors) and help‐seeking behaviours (ie, hint requests) correlated with their algebraic knowledge in an online mathematics game. The k‐means cluster analysis identified four groups of students based on their in‐game metrics, and some variabilities in their in‐game performance were found. Although hint requests were available, only a few students showed a high percentage of hint requests during their gameplay. The regression analysis revealed that students' in‐game performance and help‐seeking behavioural patterns explained a significant amount of variance in students' algebraic knowledge above and beyond their prior knowledge. Our results suggest that how students interact with the online learning game is related to their later math performance, and qualitative aspects of student behaviours captured using log data provide meaningful information to students' algebraic understanding. Practitioner notesWhat is already known about this topic Well‐designed online educational games can improve students' math knowledge, skills, and engagement. Several studies found that some in‐game metrics, such as in‐game progress or correctness of tasks, are positively associated with learning outcomes. However, many studies used simple metrics like the correctness of each task or the number of problems completed to measure students' performance in the game. Few studies measured more in the moment or qualitative aspects of student in‐game performance, for example, how efficiently students solved problems in the game. What this paper adds In‐game metrics measuring more in the moment and qualitative aspects of students' problem‐solving explained a significant amount of variance in students' algebraic knowledge above and beyond students' prior knowledge. Students' strategy efficiency and strategic planning were positively associated with students' math problem‐solving performance. Although hints were available for almost all problems, only a few students exhibited a high level of help‐seeking behaviours, and the students with the lowest in‐game performance who needed help the most showed low help‐seeking behaviours. Implications for practice and/or policy Classifying students into profiles could provide ways to identify students with different levels of problem‐solving skills and help teachers provide more personalized instruction. Teachers could use this information as a formative assessment to identify the core areas of intervention needed, particularly for students struggling with math concepts taught in the game. Instructional strategies that make hints more appealing or engaging to students might be needed.

TECHNOLOGY & PRODUCTIVITY AMONG EDUCATIONAL INSTITUTIONS INFORMATION COMMUNICATION AND TECHNOLOGY IN PUBLIC SECONDARY SCHOOLS IN MAKUENI COUNTY, KENYA

The primary objective of this research article is to identify the key impacts of integrating technology into public secondary schools in Makueni County. To achieve this, a descriptive research strategy has been employed, that brought in 140 teachers and 25 principals. Data collection was conducted via interview schedules and questionnaires that were aimed at administrators and teachers. The gathered data underwent quantitative and qualitative analysis. Descriptive statistics and mean calculations, a measure of central tendency, were utilized for data analysis. The research hypothesizes that Information and Communication Technology (ICT) plays a crucial role in enhancing educational quality. ICT enables more personalized instruction and immediate feedback, thereby improving literacy both inside and outside the classroom. The research article also examines how ICT can be used to foster creativity and higher-order thinking skills. In terms of administration, ICT is pivotal in streamlining tasks such as data collection, document processing, record-keeping, and financial document recovery, making retrieval more straightforward and administration more efficient. Furthermore, ICT aids in the efficient scheduling of school activities, including parents and teachers association (PTA) and Board of Management meetings, and managing school calendar events, classes, and exams. In a nutshell, the research article highlights the importance of ICT in disseminating information and fostering effective communication among stakeholders.

Application of Computer-Aided Technology in Teaching Spoken English

Computer-aided technology has revolutionized the teaching of spoken English by offering innovative tools and resources that enhance the learning experience. Through interactive software and multimedia platforms, learners can engage in immersive language exercises, receive real-time feedback, and access personalized instruction tailored to their proficiency levels. This paper introduces a groundbreaking approach for integrating computer-aided technology into the teaching of spoken English, utilizing Automated Probabilistic Markov Chain Machine Learning (APMC-ML). Recognizing the significance of effective communication skills in the modern world, particularly in the realm of English language proficiency, this research endeavors to enhance the pedagogical process through innovative technological means. The methodology involves the development and implementation of an interactive computer-aided system that leverages APMC-ML algorithms to facilitate language learning. This system encompasses various modules designed to cater to different aspects of spoken English acquisition, including pronunciation, vocabulary, fluency, and conversational skills. Through a user-friendly interface, learners are provided with personalized learning experiences tailored to their individual proficiency levels and learning preferences. The application of APMC-ML in teaching spoken English represents a paradigm shift in language education, offering a data-driven approach that complements traditional teaching methods. By harnessing the power of machine learning and probabilistic modeling, educators can optimize the learning experience, expedite skill acquisition, and promote greater language proficiency among learners.