Complete Learning Tasks Research Articles

“I’ll finish it tomorrow”— Four strategies teachers could use to help their students overcome procrastination

Worldwide, academic procrastination is a prevalent concern in schools because it hinders students’ achievement, health, and wellbeing. Academic procrastination refers to students voluntarily postponing, initiating, or completing assigned learning tasks despite knowing they will be worse off for the delay. Research provides strong evidence that students procrastinate learning tasks when they lack motivation and/or have insufficient emotion regulation skills. This article builds upon the findings of a recent systematic review of interventions that target reducing/eliminating procrastination. It proposes a set of four strategies teachers could implement in their practice to ensure students initiate and complete learning tasks without unnecessary delays.

Multi-task oriented team formation in online collaborative learning

Rational and personalized team formation can greatly enhance the efficiency of online collaborative learning. However, in the process of forming learning teams and completing learning tasks, online synchronous communication among team members is often overlooked, and the abilities of team members may change during task execution, which may have an effect on the completion of team tasks. To address these problems, this paper comprehensively considers the impact of learners’ online time and skill levels on team formation. We quantify the potential development space of learners’ skills and design optimization objectives accordingly based on the Zone of Proximal Development theory. Additionally, we propose a Near-Informer deep learning model to predict learners’ future online states, aiming to measure communication efficiency. Then, we propose a multi-task oriented multi-objective optimization team formation problem, and transform it into a single-objective one by using weighted aggregation of all objectives. After that, we present a Three-dimensional Monte Carlo Tree Search algorithm (TDMCTS). The experiments are conducted based on two real datasets. Compared with the baselines, our Near-informer model achieved the highest number of optimal predictions, with 22 out of 32. Besides, TDMCTS algorithm achieved more comprehensive team formation results, with average skill satisfaction degrees of 99.8% and 98.9% on two task sets, respectively, while satisfying the task requirements.

Asynchronous Federated Learning with Grey Wolf Optimization for the Heterogeneity IoT Devices

The Internet of Things (IoT) creates new options for real-time data collection and Machine Learning (ML) model development. Nonetheless, it's feasible that a particular IoT device does not have sufficient computational power to train and implement a complete learning model. However, there are significant communication costs and data security and privacy concerns associated with sending actual data to a centralised server with a lot of computational power. Federated Learning (FL) is a potential way to train ML models using low-powered devices and Edge Servers (ES) since it is a distributed ML architecture. However, the vast majority of the works in existence make the unsustainable assumption of a synchronized parameters update manner with similar IoT nodes and reliable communications networks. To increase training efficiency and accelerate the speed for heterogeneous IoT devices in an unreliable network environment, we designed an Asynchronous Federated Learning strategy with Grey Wolf Optimization (AFL-GWO) in this research. In particular, we develop a Lightweight Node Selection (LNS) technique and propose an AFL-GWO model to efficiently complete learning tasks. To ensure that diverse IoT nodes with varying computational capabilities and network connectivity are represented in the global learning aggregate, the proposed technique makes node selections on an iterative basis. We show through extensive experiments that our suggested AFL-GWO system outperforms the state-of-the-art techniques on identically and independently distributed (IID) and non-IID data distribution in a variety of contexts.

The Effect of Scaffolding and Creative Thinking Skills in an Acid and Base Learning Project on Students’ Science Process Skills

Scaffolding is a strategy given by teachers to guide students to successfully complete learning tasks. Teachers may provide either questioning, cueing, prompting, or explaining and is based on students’ actual ability. The purpose of this study was to investigate the effect of the scaffolding provision and students’ creative thinking skills on students' science process skills (SPS) in a project-based learning (PjBL) about acid and base concepts. A quasy factorial experiment research design was applied to collect data using a pre-validated SPS-observation. An experiment group from SMAN 3 in Jambi City had learnt the concepts using a scaffolding-assisted PjBL while a control group from the same school had learnt it using PjBL without scaffolding. Prior to the learning, creative thinking ability of the students was measured using a questionnaire. Data was then analyzed using a two-way Anova test assisted by SPSS software. The results of analyses showed that there was an effect of the scaffolding and the creative thinking ability on the students’ SPS. Potential interaction between those variables was also seen in affecting the SPS. It appears that the the presence of scaffolding and the initial students’ ability in creative thinking sinergetically improved the students’ SPS.

Virtual to Reality: Understanding the Role of Metaverse as a Pedagogical Strategy

The use of the metaverse in education has gained increasing attention as a potential supplement to traditional teaching methods. This study aimed to investigate the impact of experiential learning on students' intent to use metaverse learning platforms and to explore how the metaverse can support these approaches. The study included participants from three countries and used metaverse scenes, such as a restaurant or Hong Kong scene, to complete learning tasks and interact with Non-Player Characters (NPCs) to complete assessments over a semester. The results of a correlational analysis showed that pedagogical factors had a greater impact on students' intent to use the metaverse for educational purposes than technical factors, which was unexpected. However, the study also identified challenges in adapting content to match the needs and skills of students who may not be familiar with games, as well as using the metaverse for more advanced pedagogical approaches, due to limitations in integrating content. Based on the findings, recommendations for educators, designers, and researchers are provided to enhance the effectiveness of metaverse-based education. Overall, the results of this study suggest that the metaverse can be a valuable addition to traditional teaching methods.

Relationship between Academic Self-Efficacy and Test Anxiety in Middle School Students

The period of middle school is not only a stage of academic advancement but also a crucial phase for the overall development of adolescents. In recent years, the academic stress on middle school students has been on the rise, and the increasing levels of test anxiety and academic pressure have led to psychological issues among this group. This paper employs a literature review approach to retrospectively analyze a series of empirical studies exploring the relationship between academic self-efficacy and exam anxiety in middle school students. By comparing the research findings, it delves into the correlation between academic self-efficacy and exam anxiety, providing a foundation and reference for future research on test anxiety. Research conclusions: (1) Academic self-efficacy in middle school students can negatively predict test anxiety; (2) External factors significantly influencing academic self-efficacy in middle school students include teacher expectations and teacher-student relationships. To alleviate test anxiety among middle school students, positive expressions of teacher expectations for student exam performance are recommended. It is also suggested to increase positive evaluations from teachers and peers after completing learning tasks.

PENERAPAN MODEL PROBLEM BASSED LEARNING (PBL) DALAM PEMBELAJARAN MENGANALISIS TEKS BIOGRAFI PADA PESERTA DIDIK KELAS X SMK NEGERI 1 KAPETAKAN KABUPATEN CIREBON TAHUN AJARAN 2022/2023

This study is motivated by the students' low ability to analyze biography texts and their lack of motivation, which leads to their reluctance to complete learning tasks. One effective learning model that can be used is the problem-based learning (PBL) model for analyzing biography texts. The objectives of this study are: 1) to determine the effectiveness of the PBL model in teaching biography texts to class X students at SMKN 1 Kapetakan in the 2022/2023 academic year, and 2) to describe the learning activities of biographical texts using the PBL model in class X at SMKN 1 Kapetakan in the 2022/2023 academic year. This research follows an experimental method with a quasi-experimental design called the nonequivalent control group design. The data analysis shows that the learning process of analyzing biography texts in class X at SMKN 1 Kapetakan in the 2022/2023 academic year has improved after using the problem-based learning model. This is supported by the results of the initial and final tests, which were analyzed using the independent sample t-test. The t-count value is 0.3887, while the t-table value is 0.2024. Therefore, t-count (0.3887) > t-table (0.2024). Furthermore, the observation results indicate that the problem-based learning model enhances student engagement in the learning process of analyzing biography texts for class X students at SMKN 1 Kapetakan in the 2022/2023 academic year.

Principal Leadership in Increasing the Professionalism of Elementary School Teachers in Banyuasin Regency

This study aims to determine the role of the principal's leadership in improving the professionalism of teachers at a public elementary school Banyuasin III. The data source of this research comes from the results of interviews with school principals and teachers. Methods such as interviews, participant observation, and documentation are used to gather data. Data reduction, visualisation, and verification are the data analysis strategies. He has stressed the need of instructors maintaining a professional demeanour at all times in his role as an educator. After preparing a plan, their role as managers is to urge teachers to teach the content in line with the current curriculum. In their role as administrators, they also make sure that teachers have everything they need to do their jobs well and that the school or institution meets its goals. Actively supervising teachers and providing direction and guidance is part of his or her job description as a supervisor. Working as a leader, I oversee teachers as they complete learning tasks and offer guidance on the subject matter to be taught. Improving the quality of teacher learning and encouraging teachers to employ information technology in learning are two of their goals as innovators. Encourage your students to work hard and do their best in class. Motivate and urge students to study more if you want them to succeed. In its role as an entrepreneur, it collaborates closely with the school committee and strives to think of innovative ways to empower teachers so that they can improve their professionalism in the classroom.

Understanding Technological Trends in Education: How Artificial Intelligence Helps Learning in Colleges In Susta

Background. Understanding technological trends is how popular technology is used to assist learning, Artificial Intelligence is created and programmed and designed by humans to assist activities in completing human tasks. Purpose to understand technological trends in education how Artificial Intelligence helps learning in higher education in a sustainable manner, where in the last year 2020 there was Covid 19 which had spread throughout the world so there were online classes, but over the years the problem has ended, that's what caused it technology trend of Artificial intelligence in assisting learning. Method. using the method of conducting interviews from sources directly, conducting interviews with this research with the aim of getting the correct data. Results. shows that understanding the trend of Artificial Intelligence technology in helping learning in higher education in a sustainable manner is very helpful and its use has increased in recent years due to the impact of covid 19 so that it has greatly affected online learning. Conclusion it was concluded that the results of the interviews showed the use of Artificial Intelligence in assisting learning through individuals in using technology in completing learning tasks and assisting when the learning process takes place in the classroom is very helpful, technology as a medium used in learning, students still use the same technology cannot be said in sustainable

Development of an Online Learning Lesson in the Educational Technology Equipment Operation Course for Pre-Service Teachers

The objectives of this study are (1) to explore pre-service teachers’ needs in using educational technology tools for online learning, (2) to develop an online lesson about the use of educational technology tools for pre-service teachers, and (3) to compare the learning achievements before and after taking the lesson. The methodology was divided into three phases. The first phase was a needs assessment concerning the use of educational technology tools in online learning. The samples were 272 undergraduate pre-service teachers at the University of Phayao. The research instrument was a needs analysis questionnaire on the use of educational technology tools in online learning. The collected data was analyzed using Modified Priority Needs Index (PNImodified). In the second phase, an online lesson about the use of educational tools was developed. The research samples were three experts in three different fields, including instructional design, educational technology, and assessment and evaluation. The research instrument used in this phase was a suitability assessment form for the designed online lesson. Document analysis and descriptive statistics were used to analyze the data. In the third phase, the learning achievements before and after completing the lesson were compared. The samples were 112 pre-service teachers at the University of Phayao. The research instrument was a learning achievement test. The data analysis included means, standard deviations, and a t-test.
 The results were as follows: 
 
 Regarding the needs analysis concerning the use of educational technology tools in online learning, the pre-service teachers considered that tools for completing learning tasks were the most crucial element. The data collected from open-ended questions revealed that the most critical problems experienced in online learning were learning tools and poor Internet speed.
 The results from the suitability test showed that the developed online lesson was overall suitable (M = 2.98, SD = .174).The pre-service teachers’ average post-test score on the achievement test on the use of educational technology tools was higher than the average pre-test score with a statistically significant difference at the .05 level.

An artificial intelligence-driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive systems perspective

Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical research examining the adaptive and temporal characteristics of CPS, which may have led to an oversimplified representation of the real complexity of the CPS process. To expand our understanding of the nature of CPS in online interaction settings, the present research collected multimodal process and performance data (i.e., speech, computer screen recordings, concept map data) and proposed a three-layered analytical framework that integrated AI algorithms with learning analytics to analyze the regularity of groups’ collaboration patterns. The results surfaced three types of collaborative patterns in groups, namely the behaviour-oriented collaborative pattern (Type 1) associated with medium-level performance, the communication-behaviour-synergistic collaborative pattern (Type 2) associated with high-level performance, and the communication-oriented collaborative pattern (Type 3) associated with low-level performance. This research further highlighted the multimodal, dynamic, and synergistic characteristics of groups’ collaborative patterns to explain the emergence of an adaptive, self-organizing system during the CPS process. According to the empirical research results, theoretical, pedagogical, and analytical implications were discussed to guide the future research and practice of CPS.

Systematicity of students' independent work in operating systems classes

The research investigates the systematicity of students' learning activity as a parameter of the student's model, as well as the influence of systematicity on learning outcomes in the course "Operating Systems." As a theoretical framework, the importance of equipping the student himself as the subject of the educational process with the abilities and proper pedagogical forecasting tools for independent choice of the right variation of educational activity is demonstrated. Model parameters in such a pedagogical diagnostics system are proposed and analyzed. Empirical work has been completed on the basis of the learning management system Moodle and allows for the analysis of the association between students' timeliness in completing learning tasks and their educational achievements, as well as the structure of students' time planning at homework. Recommendations to improve the educational process have been suggested.

Implementation of Productive Learning Innovations in Tourism Vocational Schools

The nation's productivity and competitiveness is one of the keys to the revitalization of vocational high schools currently being implemented in Indonesia. Revitalization of Vocational High Schools can be used as a solution to produce students who are modern, dignified, innovative and creative. This study aims to find out 1) Efforts that have been made by SMK in implementing learning innovations; 2) Implementation of quality-based productive innovative learning innovations at tourism vocational schools in the Greater Bandung area; 3) An innovative learning model that is used to improve the quality of graduates through productive learning at Tourism Vocational Schools. 4) The obstacles to the implementation of graduate quality-based innovative learning found in Tourism Vocational Schools in Bandung Raya. This research is descriptive research, taking place in Tourism Vocational Schools in Bandung Raya. The sample of teachers is productive teachers who are members of the Tourism Vocational High School MGMP in the Tourism Sector of Bandung Raya Regency, totaling 30 teachers. The sample of students is the students of SMK Binawisata Lembang who are taught by one of the MGMP teachers. The instruments used were questionnaires and FGD guidelines. Data were analyzed using quantitative and qualitative descriptive results. The results showed that: 1) Not all teachers mastered the 4K concept well; 2) Not all teachers are able to implement 4K in learning; 3) 4K learning based on graduate quality, shows that not all teachers apply quality awareness in both design, implementation and evaluation; 4) the obstacles that teachers feel when learning 4K-based innovation are: integrating 4K into learning, difficulty making syntax, developing learning methods; 5) the obstacles felt by students were that most students stated that it was not so difficult to complete 4K learning tasks. The implementation of graduate quality-based innovation learning with a 4K learning orientation still requires more in-depth study, both through FGDs, class observations and review of lesson plans.

Re-FeMAT: A Reconfigurable Multifunctional FeFET-Based Memory Architecture

Most of current processing-in-memory (PIM) architectures are application specific, that is, they can only accelerate particular functions, e.g., matrix-vector dot product for neural network acceleration. However, practical applications usually involve various functions. In order to accelerate different functions, various accelerators, and dedicated circuits have been proposed. In this work, by exploring the similarities among some commonly used dedicated circuits, we adopt ferroelectric field-effect transistors (FeFETs) to build a reconfigurable multifunctional memory architecture named Re-FeMAT. Re-FeMAT is composed of multiple processing elements (PEs). Each PE is not only a nonvolatile memory array, but also can perform logic operations (i.e., the PIM mode), convolutions (i.e., the binary convolutional neural network and the convolutional neural network (CNN) acceleration mode) and content search (i.e., the ternary content-addressable memory (TCAM) mode) without changing the circuit structure. Re-FeMAT can support applications that require multiple functions. As an example, by configuring different PEs to different working modes and using a simulated annealing algorithm or a tabu search algorithm to optimize the task-PE assignment, Re-FeMAT can completely accelerate few-shot learning applications. Our simulation results based on a calibrated FeFET model show that the proposed Re-FeMAT architecture achieves better performance and power efficiency than the previous FeMAT architecture. Compared with FeFET-based single-functional circuits, though the power dissipation of Re-FeMAT is higher in some modes, the power-delay product is still smaller. Compared with a state-of-the-art FeFET-based multifunctional accelerator named attention-in-memory, Re-FeMAT achieves lower power, latency, and energy when accelerating a complete few-shot learning task.

Top-k sparsification with secure aggregation for privacy-preserving federated learning

The proposal of federated learning solves problems of data silos and privacy protection in the field of artificial intelligence. However, privacy attacks can infer or reconstruct sensitive information from the submitted gradient, which causes users’ privacy leakage in federated learning. Secure aggregation (SecAgg) protocol can protect users’ privacy while completing federated learning tasks, but it incurs significant communication overhead and wall clock training time on large-scale model training task. Thus, it is difficult to apply SecAgg in bandwidth-limited federated applications. Recently, Rand-k sparsification with secure aggregation (Rand-k SparseSecAgg) was proposed to optimize SecAgg protocol, while its optimization of communication overhead and training time is limited. In this paper, we replace Rand-k sparsification with Top-k sparsification, and design a Top-k sparsification with secure aggregation (Top-k SparseSecAgg) protocol for privacy-preserving federated learning to further reduce communication overhead and wall clock training time. In addition, we optimize the proposed protocol by assigning clients to different groups in the logical layer, which reduces the upper limit of compression ratio and practical communication overhead in Top-k SparseSecAgg. Experiments demonstrate that Top-k SparseSecAgg can reduce communication overhead by 6.25× as compared to SecAgg, 3.78× as compared to Rand-k SparseSecAgg, and reduce wall clock training time 1.43× as compared to SecAgg and 1.13× as compared to Rand-k SparseSecAgg. Thus, our protocol is more suitable in bandwidth-limited federated applications to protect privacy and complete learning task.

KONSTRUKSI HARMONISASI MELALUI MODEL PEMBELAJARAN IBK BERBASIS PENDIDIKAN MULTIKULTURAL

The diversity that exists in Indonesia is a problem such as conflict, violent discrimination both in the community and in education. Building a harmony in diversity requires the cultivation of values that uphold equality, respect differences, anti-discrimination and so on. One of them is through multicultural education which is integrated in subjects such as sociology subjects. In order for the learning objectives to be achieved, it is necessary to use an appropriate learning model to instill multicultural values. The purpose of this paper is to provide new ideas about the harmonization construct through the IBK (Group Learning Interaction) learning model based on multicultural education. The method used in this paper is the literature review method. The result of this writing is that the instilling of multicultural values in schools is integrated in every subject. One of them is sociology subjects through the IBK learning model, where teachers from planning, implementing to evaluation will be adjusted to multicultural values.The conclusion of this paper is that the harmonization construction using the multicultural-based IBK (Group Learning Interaction) model is expected to be able to instill multicultural values so that harmony will be built, and harmony in diversity starting from the school environment whose output will have an impact on the wider community. This IBK learning model emphasizes interaction activities in a study group which consists of various individuals from different backgrounds in order to complete learning tasks together through critical discussion to solve problems and find solutions by upholding the values of democracy, humanism, nationalism, equality and tolerance. Therefore, educators need to do it through the process of planning, implementing and evaluating learning.

Higher Education-Oriented Recommendation Algorithm for Personalized Learning Resource

As smart education is continuously deepened in higher education, personalized learning resource recommendation has developed into a significant research field of smart learning. Although the prediction accuracy has been improved by knowledge tracing models established on the basis of students’ historical learning data, how to design and apply personalized learning recommendation by combining classroom teaching of higher education is a great difficulty.To recommend personalized learning resources meeting the teaching requirements in higher education, the Q-LRDP-D (Learning Resource Difficulty Prediction and Dijkstra based on Q matrix) algorithm was proposed in this study. First, learning resources were modeled in accordance with Q matrix theory. Then, students’ learning difficulty was predicted through the long short-term memory (LSTM) algorithm of a learning resource difficulty prediction module, followed by cyclic prediction through combining the to-be-learned knowledge points as required by teaching units to form a directed path diagram of learning resources. Next, the least learning resources conforming to students’ learning levels were recommended using the shortest path algorithm to complete learning tasks. Lastly, the accuracy and effectiveness of the established model were verified through undergraduate teaching experiments. Results demonstrate that the modeling of learning resources in higher education on the basis of Q matrix theory is applicable to the LSTM algorithm. In comparison with the benchmark algorithm, its precision is obviously improved. With the recommendation algorithm, the average return on learning resources in the experimental class is 6.31, which is considerably higher than that in the control group. This study provides a certain reference for improving students’ learning efficiency through recommendation algorithms in higher education and teaching.

The Impact of Online Learning toward the Independence of Primary School Students During the Pandemic in Jakarta, Indonesia

The implementation of physical distancing policy serves as the basis for the administration of online learning from home, which in turn encourages the use of information necessitating the students to change their learning method. Due to the use of digital apps, students are no longer participating in face-to-face learning and therefore required to independently complete the learning tasks assigned daily by the school. The purpose of this research is to measure the impact of online learning towards the character of independence displayed by fifth-graders during the pandemic. The research collected its data using quantitative method, namely distributing questionnaires to elementary school students in Jakarta. The samples of the research are mostly students performing online learning, with the primary variables used being online learning and the character of independence. The data obtained were processed using regression analysis and t test, which were used to measure the influence and significance level of online learning towards students’ character of independence. The research reveals that the significance level is 0.000 < 0.05 and the t count value is 6.465, which is greater than the t table value of 2.002. It can be concluded that online learning affects the character of independence possessed by the fifth-graders in Jakarta. This shows that traits suggesting independence, such as being capable of performing key activities in learning, as well as realizing and understanding what needs to be done to appreciate family members, teachers and the school by acting wisely and responsibly in completing learning tasks, have become new values that they believe in. To ensure that students keep developing their character of independence during the pandemic, improvements need to be made in terms of the quality and quantity of online learning facilities. Such improvements can be done through a collaboration between the government, schools, teachers, students and the society in general.

(Retracted) Image segmentation and visualization allocation method of engineering training resources in biology training

Existing processes for visual allocation of engineering training resources utilizing computers are inefficient and extend the time needed to complete learning tasks. A new process visual allocation method in engineering training resources in a biology teaching process is proposed. The biological teaching engineering training resource perception module is set up to send the resource data to the controller through the module. By quantifying the computing power and resource consumption of each node, the nodes that currently are bottlenecks as well as nodes with potentially more computing power yet highly likely to stand idle (high-potential idle nodes) are found. Then, the optimal data allocation proportion is found through the idea of greedy search, and the amount of data actually processed by parallel instances on each node is adjusted to match its computing power. According to the dynamic partition strategy of engineering training resources, a process visual allocation model is established by using ant colony scheduling algorithm. Taking the task volume of 500 MB as an example, in the homogeneous cluster environment, the completion time of this method is 89.7 s, which is 28.6 and 30.9 s shorter than the methods based on cloud computing and mobile edge computing; in heterogeneous cluster environment, the completion time of this method is 99.4 s, which is 41.4 and 43.1 s shorter than the methods based on cloud computing and mobile edge computing. Therefore, this method reduces the task completion time and improves the efficiency of task scheduling.

IMPLEMENTATION OF INNOVATIVE ARTIFICIAL INTELLIGENCE COGNITIONS WITH PROBLEM-BASED LEARNING GUIDED TASKS TO ENHANCE STUDENTS’ PERFORMANCE IN SCIENCE

This research conceived multivariate learning concepts and aimed at the implementation of integrating artificial intelligence (AI) life learning of face recognition, self-driving, and robots’ situational issues into problem-based learning (PBL) in natural science. This research put forward problem-solving and group cooperation in which students worked together to complete learning tasks from encountering problems to planning problem-solving. All 127 participants engaged in this research from the Hungkuo Delin University of Technology in Taiwan. 7 experts helped construct the content validity, construct validity, and reliability through instrument developments of 5 open-ended test questions and 31 learning attitude items. The statistical analyses revealed that those students improved their scientific cognitive skills of problem-solving by quantitative analyzes and presented their logical reasoning and activation ability by qualitative ones. In addition, the descriptive statistical analysis of learning attitude revealed that students had positive thinking attributes for the application of cross-disciplinary PBL and AI new knowledge. These findings had implications for the AI cognitions with PBL educational environment to increase innovation in the classroom. Pedagogical suggestions presented that students’ learning growth in the environment requires long-term cultivation to exhibit significant learning outcomes in the future. Keywords: artificial intelligence, cross-disciplinary, problem-based learning, situational issues