Incomplete Learning Research Articles

대학생의 생성형 AI 이용경험에 관한 현상학적 연구

Objectives This study aims to explore the direction of Generative AI literacy education through the exploration of college students' experiences using Generative AI. Methods For this purpose, written and face-to-face in-depth interviews were conducted with 12 university stu-dents (5 male students and 7 female students) and analyzed by applying Colaizzi's phenomenological method, which consists of seven steps to explore the nature of common experiences rather than the individual participants. Results As a result of analyzing the interview data, 5 semantic themes and 14 sub-themes were derived under the essence of the experience called ‘co-evolution’. The five semantic themes consist of ‘getting to know new media’, ‘useful but dangerous existence’, ‘sharing roles with Generative AI’, ‘adapting to changes in the learning environment’, and ‘seeking a life that coexists with Generative AI’. The 14 semantic themes consisted of ‘matching up with Generative AI’, ‘creating my own method of using prompts’, ‘limitation of inanimate object’, ‘repeating unnecessary learning activities’, ‘incomplete learning assistant’, ‘my own tutor’, ‘reducing learning time’, ‘setting my own scope of use’, ‘another team member’, ‘passive new media user’, ‘recognition of reality and acceptance of new media’, ‘improving the competence to use Generative AI’, ‘selecting Generative AI according to the purpose of use’, and ‘competition with the human-specific domain’. Conclusions The discussion points on the direction of Generative AI literacy education for university students are as follows. First, it provides education in which prompts can be effectively input. Second, it provides guidelines so that the Generative AI can be used as an auxiliary tool rather than the main tool for learning. Third, it provides education on the characteristics of the Generative AI. Fourth, it enhances education on usage ethics.

An Examination of Views of Teachers, Students, and Parents on Inclusive Practices in Social Studies Lessons During Distance Education

Purpose: The aim of this study is to examine the views of classroom teachers, fourth-grade inclusion students, and parents regarding inclusive practices implemented in social studies lessons during distance education. Design/Methodology/Approach: The research design is case study. The data were obtained from a study group consisting of 15 classroom teachers, 15 inclusion students, and 15 parents during the 2020-2021 academic year. The data collected through semi-structured interview forms were analyzed using content analysis. Findings: As a result of the research, it was seen that most of the classroom teachers did not implement separate practices for inclusion students, using methods such as direct instruction, question-answer, and discussion in social studies lessons. Inclusion students stated that distance education was mostly negative, lacked social interaction, was boring, did not provide an interactive environment, and incomplete learning was experienced. According to the parents’ opinions, it was concluded that students did not participate in social studies lessons, had learning gaps, the course was not presented appropriately for the student, teacher did not provide additional support for their children, and distance education caused attention deficits in their children. Highlights: From all stakeholder perspectives, it was concluded that distance education mostly had negative effects on inclusion students. Additionally, both teachers and parents frequently expressed that the implemented practices during the pandemic were designed according to typically developing students, resulting in inclusion students being neglected.

Multi-dimensional weighted deep subspace clustering with feature classification

Deep subspace clustering (DSC) methods based on deep autoencoder (DAE) and self-expression layers have achieved impressive performance. However, traditional DSC method often loses useful information in the feature extraction of DAE, leading to incomplete learning of the self-expression coefficient matrix. Besides, existing deep subspace clustering methods always extract feature information from a single dimension, ignoring the potential information that may exist in the self-expression coefficient matrix across multiple dimensions. In this paper, we propose a novel method called multi-dimensional weighted deep subspace clustering based on feature classification (MWDSC). Specifically, we use the multivariate feature classification module (MFC) to learn the potential information of multiple local feature matrix for local pre-clustering. To further improve the clustering performance, we propose a self-flip weighted fusion strategy (SWF). The SWF strategy obtains a self-expression coefficient matrix with rich features through self-flipping weighting, thereby aiding in achieving global optimal solutions. Extensive experiments conducted on five benchmark datasets validate the superiority and effectiveness of our proposed method over other state-of-the-art approaches.

The Development Of Flash Card Learning Media in Increasing Students' Learning Motivation in Indonesian Language Learning in Grade 1 SDN 15 Koto Baru

Based on the author's observations at SDN 15 Koto Baru, especially in class 1, the author found several factors causing students' lack of focus in learning, assumed by several factors in learning, less varied learning media, lack of interest, so that students' learning motivation to participate in the learning process is still low. and paying less attention to the material being conveyed so that students do not really understand what is being conveyed so that students do not really understand what is being conveyed by the teacher. This happens because the learning tools and media used are less attractive, only using media such as blackboards and textbooks. The aim of researchers developing flash card media is to create a learning tool that can increase student motivation through the use of visuals and interactives, facilitate understanding of complex concepts in a way that is easy to understand, and stimulate student interest and motivation in studying subject matter through a fun and interesting learning experience. It can be seen that the students' learning outcomes have reached KKM 70, namely, students' learning outcomes with an average of 80% are categorized as very effective. Meanwhile, students' incomplete learning outcomes with an average of 20% are categorized as ineffective. So that flash card learning media can be applied in the learning process. The results of the analysis included 8 students who completed and 2 who did not complete, with a total of 10 students, the number of students who completed was 8 divided by the number of 2 students multiplied by 100, the result is 80%. In the KBBI, effectiveness is defined as usability, activeness, as well as suitability and the goals to be achieved. Based on the analysis above, it can be concluded that flash card learning media is effective, meaning it can help students improve learning outcomes

HYPERSPIRITUALITY OF MUSLIM TEENS LEARNING RELIGION ON THE INTERNET ERA

This study investigates three key questions: how Muslim youth interpret religion in the Internet era, how the Internet influences and constructs it, and how religion is conceptualized. This is a qualitative study. The data was gathered through in-depth interviews, observations, and documentation. This study comprised 71 Muslim teens aged 17 to 21 from Kediri Regency who were chosen for their extensive usage of the Internet to explore Islam. The analysis involved data reduction, visualization, and conclusion. Data analysis techniques included descriptive, analytical, and interpretative approaches. The study's findings revealed that Muslim teenagers who learn Islam online experience hyperspirituality due to hypersemiotics. False indications and incomplete learning due to ignorance of Islamic religious research should lead to a nihilistic interpretation of Islam. Teens are deceived by the ideology driving religious information on the Internet, which they believe is neutral. Anti-ideological ideologies undoubtedly serve ideological goals. As a result, they incorrectly feel that the lack of dogma in online Islamic education prevents them from achieving their primary purpose.

Health science students’ preparedness for climate change: a scoping review on knowledge, attitudes, and practices

IntroductionClimate change (CC) is a global public health issue, and the role of health professionals in addressing its impact is crucial. However, to what extent health professionals are prepared to deal with CC-related health problems is unclear. We aimed to evaluate the knowledge, attitudes, and practices of health students about the CC.MethodsWe conducted a scoping review through systematic searches in PubMed, Scopus, Web of Science, Proquest, and EBSCO. We included original scientific research with no language or time restrictions. Two authors independently reviewed and decided on the eligibility of the studies, then performed data extraction.Results21 studies were included, with a total of 9205 undergraduate nursing, medical, pharmacy, and public health students mainly. Most health science students (> 75%) recognized human activities as the main cause of CC. However, they perceived a lack of knowledge on how to address CC. Moreover, we found inadequate coverage or limited development of CC in related curricula that may contribute to incomplete learning or low confidence in the theoretical and practical concepts of students.ConclusionThe findings of our scoping review suggest that while health sciences students possess a general understanding of CC, there is a significant gap in their knowledge regarding its specific health impacts. To address this gap, there is a need for targeted education and training for future health care professionals that emphasizes the health effects of CC.

Religious confusion and emptiness: Evaluating the impact of online Islamic learning among Indonesian Muslim adolescents

Internet-based religious learning has presented a new face to the diversity of Muslim youth. This article aims to analyse and evaluate Muslim youth’s understanding, attitudes, and religious practices and demonstrate the impact of internet-based Islamic learning. As many as 23 Muslim youths in Jepara, Central Java, aged 17–20 years, became the informants of this study. Data were collected through in-depth interviews and observations. Further research data were analysed descriptively and interpretatively. This study found that most Muslim youths who studied Islam online experienced confusion and emptiness in religion caused by an incomplete learning process. Most Muslim youths do not have solid theological views, pseudo-worship practices and weak religious ideologies. This study suggests that teenagers acquire essential Islamic knowledge from credible sources such as kyai with clear scientific credentials. This foundation enables them to discern reliable Islamic content in cyberspace, safeguarding against distortions in their understanding of Islam and spirituality.Contribution: This research provides a more comprehensive understanding of the complexities of the impact of internet-based learning on the diverse experiences of Muslim youth. Although the internet has the potential to be an invaluable source of knowledge, the challenges of integrating information with traditional understanding require serious attention.

On Designing Prototype Model of “MathWiz” Mobile Application as an AR-Based Learning Media to Improve Students’ Mathematical Proficiency

Learning loss is one type of learning crisis, which refers to the loss of previously learned competencies, incomplete learning at the grade level, and the numerous consequences of not mastering learning at each grade level. As a result, attempts can be made to address these issues through advancing information technology, particularly through augmented Reality (AR)-based learning technologies and artificial intelligence with machine learning. This study aims to create and evaluate the effectiveness of the mobile application "MathWiz" as an AR-based learning media for improving students' mathematical proficiency. The MathWiz program is designed in three stages: listen to customers, build/revise mock-ups, and customer test drives mock-ups. Many respondents from several universities in Bandung participated in this study. According to the results, MathWiz has four advantages: (1) it can display detailed objects and learning materials in real-time; (2) interactivity supported by sophisticated and accurate audio text, video, images, and animation features; (3) has the potential to improve students' mathematical proficiency, and (4) has the potential to increase students' interest in learning mathematics and reduce boredom. The results of this study show that learning mathematics can be made more entertaining and memorable through lecturer innovation.

Analisis Penerapan Kurikulum Merdeka dalam Pembelajaran Pendidikan Pancasila SMA Negeri 17 Surabaya

The independent curriculum is expected to be a solution to increasing education gaps. The purpose of the study was to conduct an analysis of the implementation of the independent curriculum, information about obstacles, challenges in the implementation of the independent curriculum at SMA Negeri 17 Surabaya. The method used is descriptive qualitative, data collection is carried out by conducting interviews with subject teachers, vice principals for curriculum, and making observations. The results of the descriptive analysis that the implementation of Pancasila Education learning at SMA Negeri 17 Surabaya runs effectively and systematically, there are curricula, teaching materials, teaching media, as well as teaching methods, facilities, learning resources by considering the characteristics and needs of students. The obstacle is that there are still incomplete learning resources, not all teachers understand the application of the independent learning curriculum in learning.

An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images.

Deep learning-based solutions for histological image classification have gained attention in recent years due to their potential for objective evaluation of histological images. However, these methods often require a large number of expert annotations, which are both time-consuming and labor-intensive to obtain. Several scholars have proposed generative models to augment labeled data, but these often result in label uncertainty due to incomplete learning of the data distribution. To alleviate these issues, a method called InceptionV3-SMSG-GAN has been proposed to enhance classification performance by generating high-quality images. Specifically, images synthesized by Multi-Scale Gradients Generative Adversarial Network (MSG-GAN) are selectively added to the training set through a selection mechanism utilizing a trained model to choose generated images with higher class probabilities. The selection mechanism filters the synthetic images that contain ambiguous category information, thus alleviating label uncertainty. Experimental results show that compared with the baseline method which uses InceptionV3, the proposed method can significantly improve the performance of pathological image classification from 86.87% to 89.54% for overall accuracy. Additionally, the quality of generated images is evaluated quantitatively using various commonly used evaluation metrics. The proposed InceptionV3-SMSG-GAN method exhibited good classification ability, where histological image could be divided into nine categories. Future work could focus on further refining the image generation and selection processes to optimize classification performance.

University Students' Employment Anxiety and Ways of Coping in the Context of Incomplete Learning During the Pandemic Period: A Qualitative Research

University students' employment anxiety is affected by economic crises, pandemics, lack of experience, incomplete learning, unemployment rates of the country, the department of study, occupational goals and environmental factors of individuals. This study aims to determine which factors affect university students' employment anxiety and to explore the impact of learning losses experienced during the pandemic on employment anxiety and the coping strategies developed by students. For this reason, qualitative research method was used in the current study. In the research, categories and codes were created using the Maxqda 2022 package program. Research data were collected through two different methods: focus-group interviews and semi-structured interviews. The sample of the study consisted of 16 participants, 9 female and 7 male. As a result, in addition to individual and environmental factors, incomplete learning experienced with the pandemic period affects university students' employment anxiety, and students have developed various ways of coping with them.

Investigation of Preschool Teacher Candidates' Views on Zero Waste

Individuals' perspectives and views on zero waste are important in terms of minimizing the damage to the environment and a sustainable world. At this point, especially the level of educators' understanding of sustainability and zero waste will directly affect the worldview of students, who are the individuals of the future. The aim of this study is to determine and examine the views of pre-service preschool teachers on the concept of zero waste. Within the scope of the study, the views of 83 pre-service preschool teachers on zero waste were examined in depth using an interview form, and the data were analyzed by creating codes and categories. Phenomenology, one of the qualitative research methods, was used in the process. As a result of the study, it was determined that pre-service preschool teachers have limited knowledge about zero waste and sustainability. In addition, the fact that most pre-service teachers defined zero waste as recyclable waste indicates that there is an incomplete learning. It was determined that pre-service preschool teachers have limited knowledge and experience about reduce and reuse, which are recommended for a sustainable environment, and which take place before recycling in the zero waste hierarchy. Another important result was that pre-service preschool teachers prioritized especially plastic wastes in terms of recycling. In line with these results, it is recommended that these issues should be included in the education programmed for pre-service teachers.

Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

One of the most important elements of the reliability structure of a motor vessel is its power subsystem, with the most crucial component being the engine. An engine failure excludes the ship from operation or significantly limits its operation. Therefore, accurate fault diagnosis should be a crucial issue for modern maintenance strategies. In mechanical engineering, the vibration and acoustic signals recorded during the operation of the device are the most meaningful data used to identify the reliability state. In this paper, a novel system-oriented method of reliability state identification is proposed. The method consists of the analysis of the vibration and noise signals collected on each of the engine cylinders using supervised machine learning. The main novelty of this method is data augmentation application and SVM classifier implementation. Due to these aspects, the method becomes robust in the case of poor-quality data or a limited and incomplete learning dataset. The quality of the proposed identification method was evaluated by addressing a new industrial issue (Sulzer 6AL20/24 marine engine reliability state identification). During the tests, the efficiency of the method was analyzed in the case of a complete learning data set (all types of inability states were presented in the learning data set) and an incomplete learning data set (in the testing data set, there were new types of inability states). As a result, in both cases, a very high (100%) identification accuracy of the reliability state and the type of inability state was obtained. This is a significant increase in accuracy (4.6% for the complete and 22% for the incomplete learning data set) in comparison to the efficiency of the same method without the use of machine learning and data science.

EFFECT OF HYPERPARAMETERS ON DEEPLABV3+ PERFORMANCE TO SEGMENT WATER BODIES IN RGB IMAGES

Abstract. Deep Learning (DL) networks used in image segmentation tasks must be trained with input images and corresponding masks that identify target features in them. DL networks learn by iteratively adjusting the weights of interconnected layers using backpropagation, a process that involves calculating gradients and minimizing a loss function. This allows the network to learn patterns and relationships in the data, enabling it to make predictions or classifications on new, unseen data. Training any DL network requires specifying values of the hyperparameters such as input image size, batch size, and number of epochs among others. Failure to specify optimal values for the parameters will increase the training time or result in incomplete learning. The rationale of this study was to evaluate the effect of input image and batch sizes on the performance of DeepLabV3+ using Sentinel 2 A/B RGB images and labels obtained from Kaggle. We trained DeepLabV3+ network six times with two sets of input images of 128 × 128-pixel, and 256 × 256-pixel dimensions with 4, 8 and 16 batch sizes. The model is trained for 100 epochs to ensure that the loss plot reaches saturation and the model converged to a stable solution. Predicted masks generated by each model were compared to their corresponding test mask images based on accuracy, precision, recall and F1 scores. Results from this study demonstrated that image size of 256 × 256 and batch size 4 achieved highest performance. It can also be inferred that larger input image size improved DeepLabV3+ model performance.

The effect of an e-learning-based remedial program on students' learning outcomes: The case of Fraction

Students experienced difficulty in studying the topic of fraction during the covid-19 pandemic resulted in incomplete learning so remedial learning was necessary. So far, remedial learning has not been optimally implemented. Therefore, to achieve optimal remedial learning objectives, e-learning based remedial learning is needed. The aims of this study were 1) to find out the effect of e-learning-based remedial learning on the topic of fraction on learning outcomes in junior high schools in Banda Aceh, 2) to examine the improvement in student learning outcomes through e-learning based remedial learning on the topic of fraction on learning outcomes in junior high schools in Banda Aceh. This experimental study used quantitative approach. This study was designed following the pre-experimental design using one-group pretest-posttest. One-group pretest-posttest is a design that includes a pretest followed by treatment and a posttest for one group. The population of this study was all eighth grade students in Banda Aceh. The sample was randomly selected. They were the eighth grade students at four junior high schools whose pretest scores had not yet reached the KKM score of 60. The instruments used were diagnostic test questions and remedial questions on the topic of fraction. Diagnostic and remedial test results data were analyzed with SPSS. The results shows that there is a significant effect of e-learning based remedial learning on the topic of fraction on learning outcomes in junior high schools in Banda Aceh, 2) e-learning based remedial learning on the topic of fraction improves the students’ learning outcomes. The implication of this research is that teachers can implement the remedial learning based on e-learning on other learning topics, so that they has alternative solutions to the problem of implementing remedial learning outside of mathematics learning.

Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs

The temporal sequence of learning behavior is multidimensional and continuous in MOOCs. On the one hand, it supports personalized learning methods, achieves flexible time and space. On the other hand, it also makes MOOCs produce a large number of dropouts and incomplete learning behaviors. Dropout prediction and decision feedback have become an important issue of MOOCs. This study carries out sufficient method design and decision analysis on the dropout trend. Based on a large number of learning behavior instances, we construct a multi behavior type association framework, design dropout prediction model to analyze the temporal sequence of learning behavior, then discuss the corresponding intervention measures, in order to provide adaptive monitoring mechanism for long-term tracking and short-term learning method selection, and enable adaptive decision feedback. the full experiment shows that the designed model might improve the performance of the dropout prediction, which achieves the reliability and feasibility. The whole research can provide key technical solution and decision, which has important theoretical and practical value for dropout research of MOOCs.

Modeling harmony biases in learning exceptions to vowel harmony

Artificial language learning experiments typically show non-categorical results after training on categorical data. This is generally due to incomplete learning, but these results can also reveal biases. One example is that participants trained on a vowel harmony language with alternating and non-alternating affixes prefer the non-alternating affix in harmonic contexts (Finley 2021). In this paper, I show that (i) the preference for harmonic items in non-alternating affixes replicates for remote (online) data collection, and (ii) that this effect can be modeled with MaxEnt Harmonic Grammar. In Harmonic Grammar, the harmony score of each candidate determines its grammaticality, and the probability of surfacing. Because non-alternating affixes that satisfy vowel harmony have higher harmony scores than non-alternating affixes that violate harmony, harmonic candidates will be more likely to surface than disharmonic candidates, even when both types of items surface at levels greater than expected by chance. The theoretical and methodological implications for these results are discussed

Research on the Reasons for Incomplete Learning of Mathematics Textbooks by Undergraduate Mathematics Students

Recently, the China government has increasingly attached importance to the development of basic science. As the reserve talents to promote the development of mathematics, undergraduates majoring in mathematics have received the attention of researchers for their mathematics learning. However, in practice, their learning of mathematics textbooks is not comprehensive. Through interviews, coding methods, and quantitative analysis methods, the main reasons are investigated and analyzed in this paper. The conclusions are as follows: (1) The content of the textbook and the exercises at the end of the lessons are too difficult and beyond students' ability. (2) Teachers have lazy behaviors and ask too little of their students. (3) Students themselves are lazy and take exams very seriously, lacking motivation and initiative to learn. To change this situation, the mathematics textbook can be improved to explain profound theories in simple language. Teachers can also tighten the requirements for their students. In addition, mathematics majors should also improve their requirements for themselves, overcome inertia, and reasonable scheduling, and pay more attention to the use and study of mathematics textbooks.

Problematics of Muhammadiyah Learning at SMP Muhammadiyah 51 Sidikalang

The background of this research is that there are still problems in learning at SMP Muhammadiyah 51 Sidikalang regarding Kemuhammadiyahan lessons. Based on the results of observations made by researchers, one of the causes is the low interest in student learning in Kemuhammadiyahan lessons. This study aims to analyze the problems of student learning in the Kemuhammadiyahan subject, to reveal the constraints experienced by teachers and students and to explain the efforts made to overcome the obstacles that occur to the teachers and students. The method used in this research is descriptive using a qualitative approach. Respondents in this study are 2 people who have the criteria that have been explained. The data collection tools used in this study were observation sheets, interview sheets, and documentation. The results showed that the constraints experienced by the teacher were the lack of learning time and the lack of self-awareness of the students to better understand Muhammadiyah learning material. While the inhibiting factors of student learning interest are the lack of insight of the teacher when explaining learning to students to make it easier for students to understand the material taught by the subject teacher, the condition of the class is crowded, students have their own busyness when the teacher explains the material and students who are not ready to study in incomplete learning equipment. Efforts are being made to overcome the obstacles experienced by teachers and students by adding learning time so that students can calmly listen to the explanations taught by the teacher.

Examination of the Concept Images of Pre-service Teachers for Single-Variable and Multi-Variable

This study aims to examine the concept images of pre-service mathematics teachers for the concepts of single-variable and multivariable functions. In the present research, which was conducted with the case study method, a test consisting of three open-ended questions was used to obtain data about the definition of the concept of function and the concept images of the single and multivariate functions of the pre-service teachers. The data obtained were coded with the descriptive analysis method and analyzed qualitatively. Each coding was given as a theme with frequency and percentage values and the answers given by the pre-service teachers were presented as examples. The findings obtained in this study showed that the pre-service teachers' concept images about function consisted of different representations of the concept of a function and concept images were generally classified as matching, transformation and equation. The pre-service teachers' concept images of the concept of function also had incomplete learning and misconceptions. That definition and value sets, which are important components of the concept of function, were not included in the answers. The pre-service teachers defined the functions as f(x) in multivariate function examples, and they ignored that x represents the only variable in the function. In addition, pre-service teachers who had misconceptions in single-variable function examples (equation and algebraic expression) made the same mistake in multivariable functions and could not make a correct representation although they used two variables in their expressions. It was determined that misinformation and misconceptions in concept images for multivariable functions were caused by incomplete information and mislearning in the function definition and single-variable function examples. The findings suggest that supporting the course designs on single and multivariable functions with examples and concept knowledge would be beneficial for an effective learning process.