Learning Time Research Articles

The influence of e-learning on exam performance and the role of achievement goals in shaping learning patterns

Digital learning environments provide opportunities to support learning in higher education. However, it is yet unclear why and how learners use these opportunities. We propose that learners’ achievement goals and their beliefs regarding the instrumentality of e-learning tools to achieve those goals are predictive for learning behavior within digital learning environments. Furthermore, we assume learning behavior characterized by longer overall learning time, more distributed learning, and less learning delay predicts higher exam performance. To test these hypotheses, we analyzed log-file data of 91 university students who had used an intelligent tutoring system as exam preparation in a pre-registered study. Beyond the overall predictive validity of the intelligent tutoring system, we found a negative association between learning delay and exam performance. Achievement goals predicted learning time and time distribution, an association that was partly moderated by perceived instrumentality. This suggests that goals and beliefs are important puzzle pieces for understanding e-learning (behavior).

Online Learning Platforms on Efl Students’ Learning Intensity and Frequency

This The present study analyzed the role of online learning schemes in influencing EFL students’ learning time and frequency, supporting the assertion that more work on the existing engagement of the learners as well as their outcomes is necessary. Given the level of technology in the current world and the trend of going online, this study adds value by exploring the use of technology as opposed to more conventional techniques. The study utilized a mixed-method research approach combining both quantitative and qualitative research with a quasi-experimental design involving 190 university participants which made up a control group receiving instructional delivery through conventional methods while the experimental group made use of online platforms. Research Instruments include the validated questionnaire and semi-structured interviews in which quantitative data was analyzed using independent sample t-test while qualitative data was analyzed in an iterative manner. The findings show that the experimental group experienced, relative to the control group, a higher level mean score in terms of the learning intensity and the learning frequency (mean = 81.51 vs mean= 64.58) with a Sig. (2-tailed) p equal to 0.000. Preferences showed Moodle as the most popular platform used (34.21 %), next YouTube (25.26%), and the main reason for the choice of the platform was ease of use (50.53%) and Completeness of learning materials (23.68%). YouTube was perceived as the best platform for learning (38.95%) followed by Quipper (21.05%) and Podcasts (18.42%) for effective learning. It has been asserted in this study that the introduction of these platforms into the EFL teaching process will result in an improvement of both learning achievements as well as the involvement of the students.

In Search of Better Policies? The Relevance of Different Forms of Knowledge for Finnish Development Policy

Modern interpretations of Aristotelian concepts of knowing, episteme, techne and phronesis, have lately been adopted to study expertise and professions. The article argues that a combination of the three types of knowledge would be useful in development cooperation and that especially phronesis, or practical wisdom, should take the central stage as it combines practice with values. Although values are central in development work, they are currently undermined by technical understandings. The article explored the different forms of knowledge that are used and appreciated within the Finnish development cooperation. The data was drawn from a process of providing topical and rapid scientific inputs to Finnish ministry officials and it was complemented with key-informant interviews. The findings showed that although scientific knowledge was appreciated by the ministry officials, the internal incentive structures of the development administration favoured technical forms of knowledge. Especially results information as well as picking up travelling ideas from the major trendsetters within the field were important in everyday work. The main structural constraints for phronesis to flourish within the department seemed to be a lack of time for learning and reflection. Development is a messy process involving stakeholders with various interests and wicked problems that do not have simple solutions and are inflicted with power struggles. With declining funding and diminishing appreciation from the side of the power holders, the value of knowledge available to improve the quality and effectiveness of development policy and cooperation becomes important to all stakeholders.

Comparative analysis of learning motivation, strategies, and effectiveness between medical interns and PGY during the pandemic.

In the post-pandemic era, medical education faces significant shifts in learning modes. This study, employing cross-sectional research from 2021 to 2022, surveyed 214 participants, including 104 medical interns and 110 Post-Graduate Year trainees in Taiwan. Findings revealed notable differences between the groups in age and current internship hospital. Medical interns spent significantly more time in daily self-directed learning, with a higher proportion exceeding 3 hours post-work. Although weekly self-directed learning hours did not show statistical significance, more medical students studied over 7 hours weekly. In terms of learning attitudes and motivations, medical interns outscored Post-Graduate Year trainees, indicating a substantial contrast. The study suggests strategic integration of online and traditional education, tailored to course characteristics. Future research should further explore the effectiveness of online learning, aiming to optimize digital learning while preserving traditional education values.

An optimized isolation forest based intrusion detection system for heterogeneous and streaming data in the industrial Internet of Things (IIoT) networks

While conventional Intrusion Detection Systems (IDS) are essential for defending against intruders in the Industrial Internet of Things (IIoT), handling data from heterogeneous and streaming data sources should receive more attention. This work introduces a novel Optimized IForest-based Intrusion Detection System (OIFIDS) which is designed to handle both heterogeneous and streaming data efficiently. The suggested approach employs a collection of optimized binary trees, each of which is trained on a distinctive subset of data, and in which the location of empty leaves determines the anomaly score assigned to a certain data point. Optimizing isolation Forest (iForest) utilizing a modified version of the Harris Hawks Optimization algorithm, which exploits both Exploration factor and Random walk strategies (ERHHO) decreases the dataset's dimension, decreases its learning time, and enhances the detection precision, accuracy, F1-score, FPR, and recall. To demonstrate how effective the proposed approach is, it is evaluated using three datasets: CICIDS-2018, NSL-KDD, and UNSW-NB15. The experimental results prove the ability of the suggested approach in handling both heterogeneous and streaming data efficiently and delivering results that were comparable to the cutting-edge baseline techniques. Moreover, it performs effectively when there are no anomalies in the training sample and when dealing with challenging scenarios with several irrelevant features and high dimensions. Based on the comparison with various state-of-the-art IDSs, the suggested approach is able to detect intrusion with greater accuracies of 95.6%, 94.8%, and 99% than the other approaches on the NSL-KDD, UNSW-NB15, and CICIDS-2018 datasets, respectively. Experiments on heterogenous data reveal that Area Under the ROC Curve (AUC) of OIFIDS beats the baseline approach for UNSW-NB15 dataset and is higher than the second-best method by 8% and 2.4% for NSL-KDD and CICIDS-2018 respectively. Evaluating the proposed system on streaming data illustrates that it can address the concept drift problem well with high AUC value of 0.948, 0.97, and 0.922 on the NSL-KDD, CICIDS-2018, and UNSW-NB15 datasets, respectively.

Explaining the penetrating role of technology in online foreign language learning achievement

AbstractThis study aims to integrate the technology acceptance model, classroom environment theory, and positive psychology theory to explore the relationships among learners' technology acceptance (TA), perceived online classroom environment (POCE), emotional states (enjoyment, anxiety, boredom, and burnout), and academic achievement in online classes. Six hundred and sixty‐two university students learning Chinese as a foreign language participated in the survey and completed the reading test. The results of partial least square‐structural equation modeling reveal that TA influences reading achievement directly and indirectly through anxiety (with the reversed mediating effect) and enjoyment, and the online classroom environment mediated the relationship between TA and learning emotions (except anxiety). These findings suggest that technology factors may shape the affordance of online classrooms and penetrate environmental and emotional variables to impact FL learning outcomes. Multigroup analysis with different self‐online learning time underscores that enjoyment and anxiety differ in two different effect paths (POCE‐enjoyment) and (TA‐anxiety), implying enjoyment may be more environment‐related while anxiety may be more technology‐related in the online foreign language classroom. This study provides both theoretical and pedagogical implications.

How teachers develop professionally: strategies used by high school teachers in Taiwan

ABSTRACT This study used a constant comparative qualitative research method to explore formal and informal learning strategies teachers use to develop professionally and the impact these learning strategies have on their professional practices. Participants were selected through snowball sampling, consisting of 20 teachers from five high schools in Taiwan. Three were male, and 17 were female, with an average age of 43. The study found that participants’ formal learning strategies include attending workshops and conferences and doing professional courses in colleges or online. Their informal learning strategies include discussions and sharing ideas with colleagues, using the internet to learn and search for information, reading reference materials, and reflecting on teachings and students’ feedback. The impact of the learning on participants’ professional practices includes improved subject content and professional knowledge, teaching and learning skills, classroom and time man agement skills, teaching and learning aid production skills, and collaboration and information-searching attitudes.

Feasibility and Usability of an Occupation-Based Immersive Virtual Reality Protocol for Older Adults

The growing population of aging adults, coupled with the widespread adoption of technology, including within virtual environments, prompts the need to understand technology engagement preferences among older adults. This study aimed to explore the experiences and perceptions of older adults engaging with a client-centered immersive virtual reality (IVR) protocol tailored to their specific occupational interests. Employing a mixed methods approach, the research combined the quantitative System Usability Scale (SUS) assessment with qualitative interviews to examine the feasibility, usability, and learning experiences of community-dwelling older adults. Fifteen (n = 15) older adult participants trialed an IVR session tailored to match their chosen occupations of importance. Findings revealed that the usability of IVR varied significantly among participants, with an average SUS score of 55, indicating a need for further investigation into usability issues. Qualitative analysis identified three themes: IVR is usable when it is intuitive and with training, balance the apps for success, and overall lasting impressions were related to the perceived implementation for the participant. The incorporation of meaningful occupations in IVR programming is feasible and can be integrated into healthy aging-in-place programming; however, expanding available leisure-based apps and increasing the learning time before engagement should be considered.

Exploring educational transformations through the Innovative Flipped Learning Instruction Project Symposium.

The flipped classroom is an innovative pedagogy that shifts content delivery outside the classroom, utilizing in-class time for interactive learning. The preclass and in-class activities in this framework encourage individualized learning and collaborative problem-solving among students, fostering engagement. The Innovative Flipped Learning Instruction Project (IFLIP) conducted faculty development workshops over 4 years, guiding science, technology, engineering, and mathematics (STEM) faculty in integrating flipped teaching (FT) into their courses. The research aimed to assess its impact on pedagogical practices, explore its effectiveness, and provide a framework to implement FT across multiple institutions. It sought to evaluate the experiences of these educators throughout the transitional period of instructional change. In the fourth year of this project, a symposium was organized for IFLIP participants to share their experiences and findings concerning FT. This symposium helped promote collaboration among IFLIP participants and faculty interested in FT to disseminate participants' knowledge and experiences in implementing FT strategies. A survey conducted at the end of the symposium indicated that faculty participants with FT experience continued to embrace this pedagogy, and the new adopters expressed intentions to incorporate it into their courses. The survey revealed positive responses: 93% of respondents plan to integrate FT methods in future classes, 90% gained new information from the symposium and intend to implement it, and 91% are likely to recommend FT to colleagues. Ultimately, the symposium underscored the transformative impact of FT in empowering educators to deepen students' conceptual understanding, emphasizing the significance of this pedagogical approach in advancing the quality of education.NEW & NOTEWORTHY Flipped pedagogy shifts content delivery outside the classroom, emphasizing interactive learning during in-class time. The Innovative Flipped Learning Instruction Project (IFLIP) guided science, technology, engineering, and mathematics (STEM) faculty in integrating flipped teaching (FT), tracked experiences during this transition, and provided a framework for FT implementation. A fourth-year symposium fostered collaboration, revealing sustained enthusiasm for FT. The symposium underscored its transformative impact on deepening students' understanding, highlighting its significance in enhancing education quality.

Development of e-LKPD Using Flip PDF Professional on Coordination System Material for F Phase Students

Coordination system is a complex material due to its many sub-discussions. Teachers’ limited time to discuss questions with students causes students to have difficulties in understanding the material. To overcome this problem, e-LKPD which can streamline learning time and act as a solution to students' difficulties related to the lack of time to do practice questions is required. This research aims to develop an e-LKPD using Flip PDF Professional on coordination system material and analyze the feasibility of the developed product. This research uses the ADDIE development model which consists of Analyze, Design, Develop, Implement and Evaluation. The results of the research show that the e-LKPD developed obtained a percentage of 93.30% in the "very feasible" category. In the media validation stage, a percentage value of 81.67% was obtained in the "very feasible" category. Biology teachers' perceptions regarding the quality of the developed product obtained a percentage of 96.70% in the "very good" category. Small group trials with students obtained a percentage of 86.70% in the "very good" category and large group trials obtained a percentage of 89.60% in the "very good" category. Therefore, it can be concluded that the developed product is feasible and practical for students to use.

SCARF: A new algorithm for continuous prediction of biomass dynamics using machine learning and Landsat time series

We developed the SCARF (Spatial Mismatch and Systematic Prediction Error Corrected cAscade Random Forests) algorithm for continuous prediction of biomass dynamics using machine learning and Landsat Time Series (LTS). Our approach addresses the challenges posed by the cloudy subtropical forests in southern China, where monitoring biomass dynamics is notoriously difficult. To derive spectral-temporal features from the LTS, we applied the Continuous Change Detection and Classification (CCDC) algorithm (Zhu and Woodcock, 2014). Subsequently, we employed the cascade random forests machine learning algorithm for biomass prediction. This new approach corrects the spatial mismatch effects between plots and Landsat pixels as well as the systematic prediction errors in the machine learning model. As a result, it substantially enhances biomass prediction accuracy, with a coefficient of determination (R2) of 0.83 and a root mean square error (RMSE) of 6.27 Mg ha-1. In comparison, the commonly used random forests approach yields an R2 of 0.47 and RMSE of 8.52 Mg ha-1. Additionally, it provides reliable spatial prediction beyond the model-training area, achieving an R2 of 0.79 and an RMSE of 6.62 Mg ha-1. Furthermore, we demonstrate that modeling five different forest age groups separately further improves prediction accuracies, resulting in an increased R2 of 0.87 and a reduced RMSE of 3.65 Mg ha-1. A comparison of the allometric model prediction from the field plots and those from the SCARF model revealed a strong agreement, indicating that this approach can provide a temporally continuous prediction of biomass dynamics. Our study presents a robust method for continuous, reliable, and explicit spatiotemporal prediction of biomass dynamics in cloudy subtropical forests using LTS.

A new online SOC estimation method using broad learning system and adaptive unscented Kalman filter algorithm

The accurate estimation of lithium batteries’ state of charge (SOC) is important for extending battery life and preventing accidents. To improve the battery model’s adaptability to variations in actual operating conditions, this paper proposes a new hybrid SOC estimation method. The battery model is first built based on the broad learning system (BLS) to simulate the battery’s voltage characteristics. Subsequently, the adaptive unscented Kalman filter algorithm is applied for SOC estimation. We introduce the Bernstein inequality (BI) to guide the BLS model’s online update process. With the BI method, the redundant incremental data is not used for battery model updates, which improves the model’s online learning efficiency. Finally, dynamic test operation data is collected from different temperatures to validate the proposed SOC estimation algorithm. Experimental results manifest that the SOC estimation error can be limited to 0.51 %. In addition, the proposed method has satisfactory training and online learning time consumption.

Effective weight optimization strategy for precise deep learning forecasting models using EvoLearn approach

Time series analysis and prediction have attained significant attention from the research community in the past few decades. However, the prediction accuracy of the models highly depends on the models’ learning process. In order to optimize resource usage, a better learning methodology, in terms of accuracy and learning time, is needed. In this context, the current research work proposes EvoLearn, a novel method to improve and optimize the learning process of neural-based models. The presented technique integrates the genetic algorithm with back-propagation to train model weights during the learning process. The fundamental idea behind the proposed work is to select the best components from multiple models during the training process to obtain an adequate model. To demonstrate the applicability of EvoLearn, the method is tested on the state-of-the-art neural models (namely MLP, DNN, CNN, RNN, and GRU), and performances are compared. Furthermore, the presented study aims to forecast two types of time series, i.e. air pollution and energy consumption time series, using the developed framework. In addition, the considered neural models are tested on two datasets of each time series type. From the performance comparison and evaluation of EvoLearn using a one-tailed paired T-test against the conventional back-propagation-based learning approach, it was found that the proposed method significantly improves the prediction accuracy.

Retrieval practice and word learning in children who are hard of hearing

Purpose Many children who are deaf and hard of hearing (DHH) show poorer auditory word-learning skills than children with typical hearing (TH). The goal of this study was to test the effect of retrieval practice on word learning in children who are hard of hearing (HH). Method Twenty-six TH children and 16 children who are HH completed a speech perception and a vocabulary test, and a rapid word-learning game to learn new words under no retrieval, immediate retrieval, and spaced retrieval conditions. Result Linear mixed-effect models revealed that learning condition and time predicted 48% of the variance in word recognition, and age predicted 15% of word recall variance. We observed no differences in word recognition and recall between TH children and children who are HH. Children learned and remembered more words in the immediate and spaced retrieval practice conditions than in the no retrieval condition. Conclusion Retrieval practice is more effective for word learning and retention than passive exposure (no retrieval). Clinical and educational practices for children who are DHH could combine explicit instruction using targeted exposures and retrieval of new words with naturalistic approaches. Future studies should assess retrieval practice in real-life interventions to inform clinical and educational practices.

Blended Learning Instruction at Tertiary Level: Experiences of Students in One Tertiary Institution in Eswatini

This study explores the experiences of university students on the use of Blended Learning instruction at tertiary level in one tertiary institution in Eswatini. The study adopted the qualitative research approach where the case study research design was used. The study targeted students in one campus of the tertiary institution. Data were collected from a total of 15 (fifteen) students of this campus who were selected using the Simple Random Sampling technique. The data were collected using one on one interviews and were analysed using Content Analysis. The findings of the study revealed that students have varied experiences about Blended Learning. While some have pleasant experiences of this mode of learning others have unpleasant experiences. The study found that some students view Blended Learning as a good mode of instruction that offers them the opportunity to access education anywhere anytime. The findings also revealed that the Blended mode of instruction benefits students in terms of its flexibility in learning times, vast sources of information and covering a lot of content in a short period of time. This mode, however, has numerous challenges which inhibit its successful implementation. These are limited data bundles and poor internet connection which inhibit learners from accessing lessons on line. The study therefore recommends that institutions must work with the relevant network providers (Eswatini MTN and Eswatini Mobile) to provide unrestricted data and network services to students.

A Learning Dendritic Neuron-Based Motion Direction Detective System and Its Application to Grayscale Images.

In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On-Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared to traditional neural networks. This paper advances the existing model by integrating bio-inspired components into a learnable dendritic neuron-based artificial visual system (AVS), specifically incorporating mechanisms from horizontal and bipolar cells. This enhancement enables the model to proficiently identify object motion directions in grayscale images, aligning its threshold with human-like perception. The enhanced model demonstrates superior efficiency in motion direction recognition, requiring less data (90% less than other deep models) and less time for training. Experimental findings highlight the model's remarkable robustness, indicating significant potential for real-world applications. The integration of bio-inspired features not only enhances performance but also opens avenues for further exploration in neural network research. Notably, the application of this model to realistic object recognition yields convincing accuracy at nearly 100%, underscoring its practical utility.

Integrating Structured Digital Tools with the Traditional Hands-on Puzzle Method for Teaching Tooth Morphology: A Comparative Study of Educational Outcomes.

The study of tooth morphology is a critical component of the dental curriculum, highlighting the importance for dental students to acquire comprehensive and detailed knowledge of the complex structure of teeth. This study compared the educational outcomes of two student cohorts in a tooth morphology course, using traditional methods for the control group and additional digital video-based resources for the experimental group. We hypothesized that early integration of digital resources would significantly reduce the learning time. We retrospectively analyzed two groups of Master of Dentistry students. The control group (42 students) was taught using the traditional 'tooth puzzle' method, while the experimental group (42 students) supplemented traditional teaching with digital video-based tools developed by our department. Both groups' curricula culminated in a practical post-course test requiring the identification of 40 teeth, along with a mid-course test to track the students' learning progression. The number and type of incorrectly identified teeth were recorded. The mid-course test showed significant performance differences. The control group had a median (Q1, Q3) value of faults of 12.0 (7.8, 20.5), whereas the respective value for the experimental group was 4.0 (0.0, 8.0) (p < 0.001). In the control group, none achieved faultless results, with only two students (4.8%) having at most two faults, and six students (14.3%) having no more than four faults. The control group averaged 13.5 faults per student, with 19 students (45.2%) failing the test. Conversely, the experimental group showed improved performance: 12 students (28.6%) had no faults, and 25 students (59.5%) had four or fewer faults. The experimental group averaged 5.2 faults per student, with only four students (9.5%) failing. By the end of the course, both groups achieved commendable results on the practical tooth identification test. The experimental group slightly outperformed the control group, though the difference was not significant. The median (Q1, Q3) values were 0.0 (0.0, 2.5) and 1.0 (0.0, 4.5) for the experimental and control groups, respectively (p = 0.372). The students using both traditional and structured digital video-based tools showed greater learning advancement than those using only the traditional 'tooth puzzle' method.

Kei te moe te tinana, kei te oho te wairua – As the body sleeps, the spirit awakens: exploring the spiritual experiences of contemporary Māori associated with sleep

ABSTRACT For Aotearoa New Zealand Māori, sleep and wairua (spirit) are closely intertwined. During sleep the wairua awakens and journeys across multiple dimensions of time and space to attain the tools and knowledge the individual needs to navigate waking life. While this function of sleep is understood within Mātauranga Māori (bodies of knowledge regarding everything within the universe) (Hikuroa 2017), it has yet to be explored within psychological sleep research. This qualitative study contributes to addressing this gap by exploring nine Māori participants’ personal experiences of wairua during sleep. A whakapapa thematic analysis identified two interconnected layers. The first layer contributed to a spiritual explanatory framework for sleep, developed to encompass participants’ beliefs regarding wairua, which were utilised to interpret their sleep experiences. The second layer describes these interpretations, comprised of three central themes: (1) Tohu/Guidance; (2) Ako/Space and time for learning; and (3) Tau/Attaining a state of stability, peace, and purpose. These findings suggest that the spiritual experience of sleep supported participants in navigating their waking lives safely, purposefully, and meaningfully, contributing to Indigenous and Māori scholarship regarding the spiritual and cultural purpose of sleep, and with important implications for clinical, social, and academic approaches to understanding and supporting sleep.

Simulation of the behavior of fine and gross motor skills of an individual with motor disabilities

We have developed a neural network model that imitates the central nervous system’s control of motor sensors (Sánchez-Torres and Rodríguez-Romo in Neurocomputing 581:127511, 2024). Our research explored various levels of connectivity in our neural network related to neuroplasticity in the central nervous system. We have conducted a study comparing healthy individuals to those with motor impairments by utilizing reinforcement learning and transfer entropy. In our previous research (Sánchez-Torres and Rodríguez-Romo in Neurocomputing 581:127511, 2024), we have simulated human walking while encountering obstacles as an instance of gross motor activities. Now, we have used the same model to simulate fine motor activities. Our goal is to identify differences in information transmission between gross and fine motor activities among healthy individuals and those with motor impairments by evaluating the effective connectivity of our network. To regulate learning accuracy in our model, we introduced a variable called numClusterToFire. However, we discovered that the value for this variable requires careful calibration. If the value is too small, agent exploration is insufficient, and network learning is inefficient. Conversely, learning times increase exponentially, often unnecessarily if the value is too large. We conducted simulations for gross and fine motor skills using three different numClusterToFire values and found that as we increased numClusterToFire, the time required for the network to memorize the outputs for each of the objects in the test set also increased. Our findings indicate that in gross motor skills, which do not require precision, changes in the numClusterToFire variable do not affect information transfer behavior. Conversely, in fine motor skills, information transfer decreases as numClusterToFire increases. On the other hand, our model revealed that for healthy and disabled individuals, the transfer of information between the input layer and the first hidden layer is higher for fine motor skills; this important biological fact suggests the influence of external cues in performing this activity successfully. Additionally, our neural network model showed that movements that do not require precision do not necessarily require a high level of neuroplasticity. Increasing neuroplasticity may cause some neurons to transmit more information than others. Whereas, increasing neuroplasticity through practice is essential for precise movements like fine motor skills. We also found that information transfer in the network’s hidden layers is similar for fine and gross motor activities, as we observed identical patterns. However, the distribution and proportion of these patterns differ, concluding that more neurons are involved in fine motor activities, and more information is transferred compared to gross motor activities. Finally, a pattern was observed in the transfer of information in the last hidden layer, which is only present in fine motor skills. This pattern is associated with the precision of the movements.

Actualisation of Inclusive Values Through Religious Education in High Schools

This study aims to analyse the actualisation of inclusive values in religious learning and examine how the values of tasamuh, ta'awun, and justice are applied at SMA Karuna Dipa Palu, which has a diversity of ethnicities, cultures, and religions. The main objective is to assess how these values strengthen inclusive religious education and identify opportunities and challenges in their implementation. This study uses a qualitative field study method with a case study approach, collecting data through interviews, observations, and documentation analysis to understand the application of inclusivism values in religious education at Karuna Dipa High School, with data analysis carried out using the Miles and Huberman techniques which include data condensation, data presentation, and concluding. The formal-textual approach in religious learning actualises the values of inclusivity through the opening, core, and closing stages. In the opening stage, teachers from various religions prepare the class and motivate students. In the core stage, the values of openness, tolerance, mutual assistance, and justice are taught through each religion's teachings. In the closing stage, teachers and students summarise necessary materials and give assignments, thus creating solid social solidarity in the school. Karuna Dipa High School faces challenges in developing inclusivism values, such as limited technological resources, visualisation facilities for materials, and limited learning time. In addition, the lack of sustainable religious programs also hinders students' comprehensive understanding of inclusivism. This research implies that inclusivism values in religious education can strengthen social solidarity and inclusivity in diverse schools but requires increased resources, facilities, and sustainable programs for optimal results.