Learning Challenges Research Articles

Analysis of Impacts and Challenges of Online Learning During the Covid-19 Pandemic: A Case Study at SMA Negeri 2 Rantau Selatan

The COVID-19 pandemic outbreak in 2019 significantly impacted various aspects of societal life, particularly in education. The shift to online learning, necessitated by the pandemic, posed numerous challenges for teachers and students at SMA Negeri 2 Rantau Selatan. This study aims to comprehensively understand these difficulties faced by 11th-grade teachers during online instruction. Employing a qualitative descriptive approach, the findings reveal several challenges: difficulties in delivering the curriculum, evaluating students' understanding, and creating engaging online learning environments. Quantitatively, 75% of teachers reported significant challenges in curriculum delivery, 60% found it difficult to assess students' comprehension, and 70% struggled with maintaining student engagement. These findings suggest a need for more active and creative teaching methods, supported by adequate facilities from the government. The implications of these findings highlight the necessity for improved educational practices and policy recommendations to better support online learning.

Application of Scramble Learning Model to Communicative Character and Learning Interests of Elementary School Students

Education plays an important role in everyone's life. Interest in learning is a factor that encourages students to be active in the teaching and learning process so that the learning process can achieve the expected goals. The research was motivated by the low interest in leraning PKn student of class II. The purpose of this study was to analyze the effect of the application of the scramble learning model on the communicative character and learning interest of students. Teachers need to be more focused in their learning activities, have a better understanding of learning challenges, and be able to use appropriate learning models in addition to paying attention to student learning interests. The learning model is very beneficial for both educators and learners. Learning models can serve as roadmaps and points of reference for educators as they apply learning methodically. Scramble cooperative learning is another approach to solving this problem. In this study, descriptive qualitative methods were used with qualitative research. Based on the results of the study, it was found that there was an increase in the results of students' learning interest and communication skills. Students are encouraged to talk, exchange ideas, and support each other in understanding and solving problems. Keywords: Communication Skills; Scramble Learning Model; PKn (Civic Education) Learning

Genotype-Phenotype Correlation in Neurofibromatosis Type 1: Evidence for a Mild Phenotype Associated with Splicing Variants Leading to In-Frame Skipping of NF1 Exon 24 [19a

Neurofibromatosis type 1 (NF1) is an autosomal dominant neurocutaneous disorder caused by loss-of-function variants in the NF1 gene. As of 20 November 2023, over 5000 distinct pathogenic or likely pathogenic variants have been reported in public databases. However, only a few NF1 genotype-phenotype correlations have been established so far. In this study, we present findings on 40 individuals with NF1, comprising 26 unrelated probands and 14 affected relatives, who carry one of nine NF1 heterozygous pathogenic splicing variants, all of which result in the in-frame skipping of exon 24 [19a] (NM_000267.3:r.3114_3197del, p.Asn1039_Arg1066del). These variants include c.3114-2A>G, c.3114-1G>A, c.3196A>G, c.3197G>A, c.3197G>T, c.3197+1G>A, c.3197+1G>T, c.3197+2T>C, and c.3197+3A>T. Among individuals with these variants, none exhibit externally visible plexiform neurofibromas, histopathologically confirmed cutaneous or subcutaneous neurofibromas, symptomatic spinal neurofibromas, or symptomatic optic pathway gliomas. The most prevalent, and sometimes sole, clinical feature observed in this cohort is multiple café-au-lait macules, with or without skinfold freckles: 85% and 60.5% of the individuals display six or more café-au-lait macules and freckles, respectively. In comparison to established NF1 genotype-phenotype correlations, these patients demonstrate highly similar clinical presentations to those associated with the NF1 pathogenic variant c.2970_2972del (p.Met992del), known for resulting in the mildest clinical features. Despite the generally mild phenotype, cognitive impairment, developmental delay, and/or learning difficulties are still observed in 33.3% of these patients, suggesting that learning challenges remain a prominent aspect of the phenotypic presentation in these individuals and necessitate specialized care. This newly established genotype-phenotype correlation will assist clinicians in improving the management of patients harboring NF1 exon 24 [19a] skipping variants and provide a new therapeutic target for NF1 treatment.

Knitting Tradition and Mathematics: Systematizing the Literature on Indonesian Traditional Games and Mathematical Dimensions

Mathematics presents significant learning challenges for students globally, which is often reflected in poor academic performance. This research explores the potential of traditional Indonesian games to enhance math learning, an under-explored approach that can also contribute to cultural preservation This research investigates how traditional Indonesian games can enhance math learning. We conducted a Systematic Literature Review, selecting articles through keyword searches in the Google Scholar database. The criteria used were articles on Indonesian traditional games. The result of this study is that traditional Indonesian games can enhance math learning by: (1) Presenting a strong rationale that the use of Indonesian traditional games in mathematics learning is often motivated by difficulties in learning mathematics as well as cultural preservation; (2) Integrating traditional games in mathematics learning according to students' characteristics, especially at the elementary level; (3) Applying relevant mathematics topics, such as geometry and number, in traditional games; (4) Integrating traditional games that are widely known by students, such as engklek and congklak, in mathematics learning; (5) Apply the presentation technique of integrating traditional games and mathematics through learning activities at school with teachers; (6) Realize that the integration of traditional games in mathematics learning affects students' affective, cognitive, psychomotor, and skill abilities that will improve their mathematics abilities.

Degree Apprenticeship Education for Future Engineering Leaders: Developing reflective practice with work-based learning.

The significant growth in demand and offerings for degree apprenticeships (DA) since their introduction by the UK Government in 2014 is shaping the development of the workplace learning environment. In support of this, educational reflective practices within the workplace, together with associated academic credit requires greater research focus. For level 7 postgraduate degree students, it is relevant to examine enhancing reflective practices for work-based learning and in doing so, this paper focuses on a specific Engineering Business Management Degree Apprenticeship case study. This research discusses the outcomes for students resulting from the postgraduate DA course, with an emphasis on their work-based learning and reflective practices, investigating the research question “how can an apprenticeship degree integrate work-based learning with reflective practice?” A sample group of students close to graduation were chosen for the intervention and asked two question types covering their positive experiences and potential improvement areas for informing future practice. The positive findings highlighted the benefits of work-based learning and reflective practice for the degree apprenticeship, including the application of learning directly into the workplace and the addition of skills and behaviour competencies, whilst still achieving the master’s level knowledge and learning competencies. Potential improvement areas focus on the challenges of understanding reflective learning in the workplace, the work-life balance combining further education and finally the additional support required from employees. The resulting recommendations for academics and course leaders encompass achieving reflective learning within the curriculum, designing courses to reflect the student’s time demands when including work-based learning, designing for the KSB’s (Knowledge, skills and behaviours) and understanding the wider student network of employer and workplace environment.

Exploring the Role of Mental Preparedness and Emotional Competence in Nurturing Academic Success among Students in the Post-Pandemic Instructional Landscape

This study examines the relationship between students' mental preparedness, emotional competence, and their academic success in the context of the reintroduction of full in-person instructional setup after a hiatus of almost three years. Using a descriptive correlational research design, the study aimed to provide a comprehensive understanding of these variables. The Grade 11 students from Langgal National High School were selected as the respondents. The findings highlight the significant influence of mental preparedness on students' performance in academic activities, underscoring the importance of being mentally prepared to address learning challenges, especially in the context of the pandemic. Additionally, the study reveals a moderate association between the level of emotional competence and academic success, emphasizing its role as a personal resource in adapting to the learning environment. Overall, the results suggest that students' mental preparedness and emotional competence during full in-person classes significantly contribute to their likelihood of achieving academic success.

PELATIHAN GURU DALAM PENGGUNAAN PLATFORM PEMBELAJARAN DARING DI SMP SANTO FRANSISKUS ASISI PONTIANAK

At the beginning of 2020, the world faced the significant impact of the coronavirus (COVID-19) pandemic, including on the education sector in Indonesia. The shift to online learning became a crucial policy in response to the pandemic emergency. Training on the use of online learning platforms, particularly Google Classroom, at SMP Fransiskus Asisi Pontianak Utara was the focus of this community service. The aim was to enhance teachers' competence in managing online learning and assist parents in supporting their children's learning at home. The method involved direct training by competent instructors, accompanied by question-and-answer sessions and in-depth material exploration. The results showed a significant improvement in teachers' understanding and skills in using Google Classroom, as well as a positive effect on student interaction and learning quality. Recommendations for the future include continuing similar training programs with sustained support and mentoring. The outcomes of this community service have important implications for enhancing the quality of online learning during the pandemic and beyond, as well as preparing teachers to face the challenges of hybrid learning. Therefore, this service has high relevance in the context of learning in the current digital era.

STOP OVERFITTING WITH PROVEN TECHNIQUES

Overfitting is a common challenge in machine learning where a model learns the noise in the training data rather than the underlying pattern. This results in excellent performance on the training data but poor generalization to new, unseen data. In this article, we delve into effective strategies and best practices to mitigate overfitting and build more robust machine learning models.

Blended Learning And Lab Reform: Self-Paced Sotl And Reflecting On Student Learning

As part of a large exercise physiology laboratory (lab) reform project, we used blended learning to support graduate teaching assistants and lab technicians in developing their pedagogical knowledge and create an entry point to reflective conversations about teaching and learning. Because self-paced asynchronous online modules can enable reflective and self-determined learning, this asynchronous professional development course is punctuated with reflective questions for the instructional team preparing to teach reformed exercise physiology labs. Asynchronous course content was shared via short videos, podcasts, and readings. We debriefed this self-paced, SoTL-informed course together, in-person. This social debriefing kicked off our weekly synchronous reflective conversations about teaching and learning in a community of practice. Developing a shared language for talking about teaching, enabling student learning, practicing effective teaching, and beginning to contemplate teaching philosophies were described by graduate teaching assistants as notable aspects of this blended learning journey. Lab technicians described discovering SoTL and discussing learning challenges as helpful to their teaching.

Challenges in Multi-Task Learning for fMRI-Based Diagnosis: Benefits for Psychiatric Conditions and CNVs Would Likely Require Thousands of Patients

Abstract There is a growing interest in using machine learning (ML) models to perform automatic diagnosis of psychiatric conditions; however, generalising the prediction of ML models to completely independent data can lead to sharp decrease in performance. Patients with different psychiatric diagnoses have traditionally been studied independently, yet there is a growing recognition of neuroimaging signatures shared across them as well as rare genetic copy number variants (CNVs). In this work, we assess the potential of multi-task learning (MTL) to improve accuracy by characterising multiple related conditions with a single model, making use of information shared across diagnostic categories and exposing the model to a larger and more diverse dataset. As a proof of concept, we first established the efficacy of MTL in a context where there is clearly information shared across tasks: the same target (age or sex) is predicted at different sites of data collection in a large fMRI dataset compiled from multiple studies. MTL generally led to substantial gains relative to independent prediction at each site. Performing scaling experiments on the UK Biobank, we observed that performance was highly dependent on sample size: for large sample sizes (N>6000) sex prediction was better using MTL across three sites (N=K per site) than prediction at a single site (N=3K), but for small samples (N<500) MTL was actually detrimental for age prediction. We then used established machine learning methods to benchmark the diagnostic accuracy of each of the 7 CNVs (N=19-103) and 4 psychiatric conditions (N=44-472) independently, replicating the accuracy previously reported in the literature on psychiatric conditions. We observed that MTL hurt performance when applied across the full set of diagnoses, and complementary analyses failed to identify pairs of conditions which would benefit from MTL. Taken together, our results show that if a successful multi-task diagnostic model of psychiatric conditions were to be developed with resting-state fMRI, it would likely require datasets with thousands of patients across different diagnoses.

Gamified immersive safety training in virtual reality: a mixed methods approach

PurposeThe reemergence of immersive virtual technology (IVR) provides both opportunities and challenges for workplace learning (WPL). The purpose of this study is to explore and develop knowledge about how gamification influences the WPL experience by addressing two research questions: RQ1. What characterizes a gamified immersive safety training experience with IVR technology? and RQ2. How does gamified immersive safety training with IVR technology impact the WPL experience?Design/methodology/approachThe study adopted a mixed methods approach by combining a systematic literature review with a case study on an empirical project about immersive fire safety training for train operators that are used at the Swedish train operating company SJ. The case study included data from semistructured interviews, Web survey and observation studies. The data was analyzed in two stages combining inductive and deductive data analysis for identifying themes and categories.FindingsThe findings of the study are twofold: (1) themes that conceptualize the gamified immersive safety training experience based on outputs from both the literature review and the first round of data analysis; and (2) a framework with three overarching categories that are mapped with the identified themes, and which were deduced throughout the second round of data analysis.Originality/valueThe originality of the findings stresses the implications of how a body of knowledge that synthesizes gamification concepts with immersive safety training, can inform the design of WPL experiences that are facilitated with IVR technology. As such, the implications of the findings are targeted toward both the advancement of the IVR discourse in the WPL field, but also toward practical considerations for design of immersive learning experiences that enrich WPL practices and culture.

A retrospective study of deep learning generalization across two centers and multiple models of X-ray devices using COVID-19 chest-X rays

Generalization of deep learning (DL) algorithms is critical for the secure implementation of computer-aided diagnosis systems in clinical practice. However, broad generalization remains to be a challenge in machine learning. This research aims to identify and study potential factors that can affect the internal validation and generalization of DL networks, namely the institution where the images come from, the image processing applied by the X-ray device, and the type of response function of the X-ray device. For these purposes, a pre-trained convolutional neural network (CNN) (VGG16) was trained three times for classifying COVID-19 and control chest radiographs with the same hyperparameters, but using different combinations of data acquired in two institutions by three different X-ray device manufacturers. Regarding internal validation, the addition of images from an external institution to the training set did not modify the algorithm’s internal performance, however, the inclusion of images acquired by a device from a different manufacturer decreased the performance up to 8% (p < 0.05). In contrast, generalization across institutions and X-ray devices with the same type of response function was achieved. Nonetheless, generalization was not observed across devices with different types of response function. This factor was the key impediment to achieving broad generalization in our research, followed by the device’s image-processing and the inter-institutional differences, which both reduced generalization performance to 18.9% (p < 0.05), and 9.8% (p < 0.05), respectively. Finally, clustering analysis with features extracted by the CNN was performed, revealing a substantial dependence of feature values extracted by the pre-trained CNN on the X-ray device which acquired the images.

Survey results of COVID-19 pandemic on female surgical trainees: New normal work environment

BackgroundThe COVID-19 pandemic has had a profound impact on surgical training globally. We aimed to explore and identify the specific challenges faced by women surgeons during the pandemic and provide recommendations for improvement. MethodsA survey was conducted among trainee members of the Association of Women Surgeons, assessing various aspects of clinical training, mental well-being, and personal and professional life. ResultsThe respondents were distributed across the United States, with the majority (28%) from the Midwest and Northeast. Training settings were predominantly academic university hospital programs (85%). The majority (92%) were resident trainees and 32% were in research. General surgery, constituting 86% of the respondents, was the most common specialty. There was a decline in surgical cases, research, mental health, and quality of didactics. Limited learning opportunities and challenges in job search were reported. Although virtual conferences were deemed affordable, the lack of networking was noted to be significant. ConclusionThe study highlights the need for ongoing support and adaptation in surgical training programs. These programs include the optimization of virtual platforms, prioritizing mental well-being, and ensuring equal opportunities. Strategies to mitigate the impact of future disruptions and promote gender equality are essential. Further research and workflow changes are warranted for effective capacity building.

A critical review of data mining in education on the levels and aspects of education

Purpose This systematic literature review aims to identify the pattern of data mining (DM) research by looking at the levels and aspects of education. Design/methodology/approach This paper reviews 113 conference and research papers from well-known publishers of educational data mining (EDM) and learning analytics-related research using a recognized literature review in computer science by Carrera-Rivera et al. (2022a). Two major stages, planning and conducting the review, were used. The databases of Elsevier, Springer, IEEE, SAI, Hindawi, MDPI, Wiley, Emerald and Sage were searched to retrieve EDM papers from the period 2017 to 2023. The papers retrieved were then filtered based on the application of DM to the three educational levels – basic, pre-tertiary and tertiary education. Findings EDM is concentrated on higher education. Basic education is not given the needed attention in EDM. This does not enhance inclusivity and equity. Learner performance is given much attention. Resource availability and teaching and learning are not given the needed attention. Research limitations/implications This review is limited to only EDM. Literature from the year 2017 to 2023 is covered. Other aspects of DM and other relevant literature published in EDM outside the research period are not considered. Practical implications As the current trend of EDM shows an increase in zeal, future research in EDM should concentrate on the lower levels of education to identify the challenges of basic education which serves as the core of education. This will enable addressing the challenges of education at an early stage and facilitate getting a quality education at all levels of education. Appropriate EDM techniques for mining the data at this level should be the focus of the research. Specifically, techniques that can cater for the variation in learner abilities and the appropriate identification of learner needs should be considered. Social implications Content sequencing is necessary in facilitating an easy understanding of concepts. Curriculum design from basic to higher education dwells much on this. Identifying the challenge of learning at the early stages will facilitate efficient learning. At the basic level of learning, data on learning should be collected by educational institutions just as it is done at the tertiary level. This will enable EDM to accurately identify the challenges and appropriate solutions to educational problems. Resource availability is a catalyst for effective teaching and learning. The attributes of a learner will enable knowing the true nature of the learner to determine the prospects of the learner. Originality/value This research has not been published in any journal. The information presented is the original knowledge of the authors. However, a pre-print of the work is in Research Square.

Research on The Reform of University Education and Teaching Mode Driven by Artificial Intelligence

Artificial intelligence is becoming a key force leading the reform of higher education and teaching. This paper systematically analyzes the application demand and development trend of artificial intelligence in college education, discusses the reform direction and realization path of college education and teaching mode driven by artificial intelligence, analyzes the effectiveness and challenges of the reform of education and teaching mode enabled by artificial intelligence, and looks forward to the future development trend. It is found that artificial intelligence can promote the intellectualization of teaching content, the personalization of teaching process, the intellectualization of teaching evaluation, the reshaping of the roles of teachers and students, and the intellectualization of teaching management. However, there are still some challenges in knowledge expression, adaptive learning, learning analysis and acceptance of teachers and students, which need to be solved by specific measures. In the future, artificial intelligence will further enable the reform of college education and teaching mode, and colleges and universities should take the initiative to embrace artificial intelligence, optimize the teaching mode, and provide students with more intelligent and personalized high-quality education services.

Learning and achieving basic mental health competence in placement studies with the support of a tool: A qualitative study of student nurses’ experiences

BackgroundLearning basic mental health care competence is often challenging for the bachelor of science student nurses, and many lack basic mental health care competence to ensure safe and confident mental health care practice. Mental health assessment is an integrated part of this competence. ObjectiveThe objective of this study was to explore and describe in depth how student nurses experience learning and achieving basic mental health competence while on mental health placement with the support of a learning tool. DesignAn explorative and descriptive qualitative design was conducted to gain insight on how student nurses experienced learning basic mental health competence when on placement. SettingA diversity of mental health placement settings in which student nurses were involved with patient care or welfare were approached; general psychiatric wards (n = 2), psychiatric ward for elderly people (n = 1), community mental health in-patient facilities (n = 2) and unconventional placements in the community (n = 9). Unconventional placements are a diversity of non-clinical service contexts. ParticipantsThe participants comprised student nurses in their 3rd and final year while on mental health placement. Potential participants received information from course coordinators, the online learning platform, and from teachers in plenary. Using purposive sampling, 14 student nurses were recruited. MethodsIndividual semi-structured interviews were conducted online and in person at two campuses of one university in Norway between August 2020 and December 2021. The interviews were transcribed and thematically analysed as described by Braun and Clarke. ResultsStudents expressed insecurity in a new clinical context. They engaged in new learning situations and realized the diversity of nursing practice. Unconventional placements were described as challenging contexts for learning basic mental health care competence. ConclusionsThis qualitative study provided insight into how student nurses experience learning mental health assessment, and gaining relational, communicative, and ethical competence while on placement. Students revealed their insecurities and challenges in learning in a new context. Awareness of clinical learning opportunities on placement when preparing student nurses to learn basic mental health competence may help improve their confidence.

Additive and Causal Connectives in GFL Argumentative Writing

The main objective of foreign language learning and teaching is the development of communicative language competence. One of its key components is discourse competence which consists of three main elements: cohesion, coherence and text composition (Bagarić Medve and Pavičić Takač 2013). The development of writing skills is acknowledged as one of the main challenges in foreign language learning and teaching, which includes the appropriate usage of cohesive devices in order to tackle the semantic relationships within a text. In German, explicit cohesive devices refer to coordinating conjunctions, subordinating conjunctions and adverbial connectors. While a bulk of previous research has focused on English as a foreign language, the studies conducted among the learners of German are still scarce Earlier studies (e.g. Breindl 2016; Vaakanainen and Maijala 2022) pointed to a limited range of variations within the same semantic group and an inappropriate use of connectives when comparing their usage between native speakers and learners of German as a foreign language (GFL). The aim of the present study is to analyse the frequency, variation and errors of causal and additive connectives (coordinating conjunctions, subordinating conjunctions and adverbial connectors) in GFL argumentative writing. The corpus consists of 43 argumentative essays written by Croatian GFL students at B2 level. The findings concerning frequency show that additional connectives are most frequently used within the whole corpus, whereas causal connectives take third place. The additive coordinating conjunction und is the most frequent additive item, the subordinating conjunction weil is the most frequently used causal item. The results connected to variation point to a wider range in the use of additional connectives in comparison to causal, but also reveal more error recognition within the additive semantic group. The most common types of errors concerning connectors detected in the corpus are as follows: syntax, meaning, spelling/punctuation, redundancy and register. In order to utilize these research findings aiming at improving the writing skills of GFL students in the learning/instructional process, practical implications are presented at the end.

Machine Learning Applications of Quantum Computing: A Review

At the intersection of quantum computing and machine learning, this review paper explores the transformative impact these technologies are having on the capabilities of data processing and analysis, far surpassing the bounds of traditional computational methods. Drawing upon an in-depth analysis of 32 seminal papers, this review delves into the interplay between quantum computing and machine learning, focusing on transcending the limitations of classical computing in advanced data processing and applications. This review emphasizes the potential of quantum-enhanced methods in enhancing cybersecurity, a critical sector that stands to benefit significantly from these advancements. The literature review, primarily leveraging Science Direct as an academic database, delves into the transformative effects of quantum technologies on machine learning, drawing insights from a diverse collection of studies and scholarly articles. While the focus is primarily on the growing significance of quantum computing in cybersecurity, the review also acknowledges the promising implications for other sectors as the field matures. Our systematic approach categorizes sources based on quantum machine learning algorithms, applications, challenges, and potential future developments, uncovering that quantum computing is increasingly being implemented in practical machine learning scenarios. The review highlights advancements in quantum-enhanced machine learning algorithms and their potential applications in sectors such as cybersecurity, emphasizing the need for industry-specific solutions while considering ethical and security concerns. By presenting an overview of the current state and projecting future directions, the paper sets a foundation for ongoing research and strategic advancement in quantum machine learning.

Deep learning in data science: Theoretical foundations, practical applications, and comparative analysis

Deep learning has emerged as a transformative technology in data science, revolutionizing various domains through its powerful capabilities. This paper explores the theoretical foundations, practical applications, and comparative analysis of deep learning models. The theoretical foundations section discusses key neural network architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, highlighting their unique capabilities in processing different types of data. Optimization algorithms crucial for effective training, including Stochastic Gradient Descent (SGD) and Adam, are examined. Regularization techniques for preventing overfitting and enhancing generalization are also addressed. Practical applications in healthcare, finance, and retail showcase the real-world impact of deep learning. A comparative analysis of performance metrics demonstrates the superiority of deep learning models over traditional methods. Despite their advantages, deep learning models face limitations and challenges, including data dependency and interpretability issues. The paper concludes by emphasizing the ongoing research efforts to mitigate these challenges and ensure the continued advancement of deep learning in data science.

Digital Escape Room: Students’ Perceptions as an Educational Tool in Learning Reading Skills

Conventional teaching method that lacks engaging activities for students could become a challenge in learning. Though past literature on the use of digital escape room in classroom is growing, studies related to digital escape rooms in the field of social science such as English language are still in need. As students’ beliefs and perceptions are important in the ability to learn the language, this paper seeks to study students’ perceptions of digital escape room as an educational tool in learning reading skills. This study aims 1) to explore the students’ perceptions of digital escape room as an educational tool in learning English reading skills and 2) to discover the difference in the mean level of perceptions for differing genders and faculties. A quantitative study was carried out on 212 ESL tertiary students from three faculties. Data from questionnaires was tabulated and analysed using Statistical Package for the Social Science (SPSS). The findings revealed that students’ perceptions of the digital escape room in learning reading skills were optimistic. They viewed the escape room as fun, helpful and useful to be applied in other courses as well. It was also unveiled that there was no significant difference in gender or faculty among students who played the escape room. The results of this work provide empirical evidence that digital escape room can serve as a learning method in teaching English reading skills that can benefit students regardless of their gender and educational backgrounds.