Learning Output Research Articles

Evaluation of Learning Efficiency of Massive Open Online Courses Learners

This study selected 175 massive open online courses (MOOC) learners of the School of Marxism in a university in Henan Province as respondents. A hierarchical clustering analysis was carried out using SPSS22.0, and the learning styles of learners were classified by k-means clustering. The learning efficiency of learners was estimated by data envelopment analysis (DEA), and the differences of learning styles in learning inputs and learning outputs were analyzed through a variance test. Results demonstrated that according to hierarchical clustering analysis, the learning behavioral indicators of MOOC learners could be divided into four classes. According to the results of k-means clustering, learning styles could be divided into four types, namely, high-input-high-output, high-input-low-output, low-input-high-output, and low-input-low-output. Clustering results could explain significant differences in learning inputs well, thus showing significance (P<0.05). A total of 125 respondents were non-DEA effective, accounting for 71.43%. Moreover, 114 respondents had fixed or increasing returns to scale, accounting for 65.14%. The conclusions of this research are of important significance to analyze the progress effectiveness of students, increase the scientificity and rationality of teaching evaluation theory, train teaching managers to control the teaching effects, and make scientific evaluations of the learning efficiency of university students.

Deep Learning-Based Service Distribution Model for Wireless Network Assisted Internet of Everything

Internet of Everything (IoE) provides scalable service support for a heterogeneous class of users and applications. Concorde service delivery and quality are the focused problems retaining the user's satisfaction in the next-generation Wireless Networks (WNs). However, the flexible support for applications and users remains biased due to WN connections and unstable network architectures. This article introduces a Mutable Service Distribution Model (MSDM) for providing unified IoE application response. The proposed model handles the bias in IoE supporting WN connections and service drops between successive intervals. In this process, deep recurrent learning identifies the continuity between different intervals, preventing service overlapping. The service overlapping that degrades the Quality of Service (QoS) is distributed for different resource providers and WN architectures for delay-less responses. The bias is monitored by the learning output, ensuring re-assignment of application/ user service requests. Therefore, the service response drops are reduced with controlled time, aiding flexibility. Besides, the proposed model's performance is verified using response ratio, overhead, and assigning time.

Analisis peran guru dalam memotivasi belajar peserta didik kelas iv sekolah dasar

<p class="Abstract"><em><span lang="EN-GB">In education, efforts are made to improve the quality of teaching and learning materials and methods. In this case, the teacher's effort to be able to increase the effectiveness and quality of learning is by making various plans for learning activities in the classroom. Thus it is necessary that the role of the teacher can increase student motivation and increase student learning output which also personally increases teacher performance. The purpose of this study was to knowing the teacher's role in motivating the fourth grade of elementary school students' learning. This research was conducted in class IV elementary school. The research method used is descriptive qualitative with a case study approach. The data were collected by means of interviews, observations, and questionnaires from teachers and students. The results showed that it can be concluded that many students have been motivated to learn because the teacher has carried out several activities that indirectly increase motivation in learning.. The role of the teacher in increasing the learning motivation of fourth grade elementary school students has been carried out by the teacher well by increasing the performance as a teacher of course also with student initiative, of course it can also increase student learning motivation.</span></em></p>

PEMBELAJARAN ENTREPRENEUSHIP DENGAN METODE DESIGN THINKING PADA SISWA SISWI SMAK FRATERAN SURABAYA

Entrepreneurship education must be carried out from an early age. The imbalance in the number of productive labor forces with the availability of jobs will cause unemployment. One way to reduce unemployment is to create entrepreneurs through entrepreneurship education from an early age. SMAK Frateran is a school that strives to create graduates who are able to answer the challenges of the future. SMA Frateran specifically makes entrepreneurship education taught from an early age through learning with gradual materials, especially with the design thinking method. The design thinking method starts with redefining the problem and creating innovative solutions for prototyping and testing. It is hoped that the design thinking method will provide entrepreneurship education learning by looking at environmental problems and solutions by making a business project as an output of learning.
 Keywords: entrepreneurship, entrepreneurship education, design thinking

Incorporating Multiple Intelligences in L2 Writing Classes: New Horizons in Redefining the Classroom

This article seeks to restore the notion of the classroom in light of emerging pedagogies. It attempts to provide a sketchy introduction of the theory of multiple intelligences and its potential gains in ELT. Despite the variance of teaching methods and approaches implemented in classrooms, students’ results in some topics have remained below the par of teachers’ expectations. This article attempts to provide a solution to teaching students to their differences and learning preferences. It investigates the impacts of accommodating students’ intelligences profiles in writing classes. To fulfill this objective, 114 male and female participants majoring in English at the University of Kairouan were asked to write a five-paragraph essay and respond to a writing strategies questionnaire, a multiple intelligences inventory. The collected data were statistically analyzed using Pearson Correlation and ANOVA techniques to probe possible correlations and predictability levels between MI profiles and WS. The findings indicated that there is a significant degree of correlations between learners’ multiple intelligence profiles and the writing strategies they use when writing. As for the impacts of multiple intelligences on writing quality, the results revealed weak or no significant correlations. It is then suggested that the same study should be further elaborated within the same L2 context with larger population to acknowledge learners’ differences and learning preferences and benefit from new dimensions in teaching paradigms Chronic deterioration of learners results in different language topics The current upheaval caused by the striking pandemic Lack of homogeneity in the syllabi provided to university students For the above reasons, implementing the Multiple Intelligences Theory (Gardner 1983/1999) and Computer Assisted Language Learning at the university level classroom could boost the teaching/ learning and would reduce apprehension and foster learning achievements. So, knowing learners’ individual differences and preferences and integrating CALL in the classroom would allow teachers to establish “broad range of teaching strategies with their students” (Armstrong, 2009). Gardner (2006) also concludes that “people have very different kinds of minds… then education which treats everybody the same way, is actually the most unfair education”. Briefly, incorporating intelligences whether cognitive or artificial and recognizing the blended learning would revolutionize the notion of the classroom and reduce inconveniences between the teaching input and the learning output. Therefore, the new CLASSROOM would be effective, inspiring, and prospective.

Analisis pemanfaatan e-learning madrasah: studi kasus di kabupaten Indramayu

E-learning madrasah is a learning media that aims to facilitate learning activities in order to get good learning outputs during the covid-19 pandemic. The results of a preliminary study conductedon all MTs Negeri in indramayu districts during covid-19 showed that there were still many teachers who had not yet utilized this learning media in the learning they have done and the output of learning outcomes had decreased. This decrease can be seen from the average repetition of 54 from the Minimum Completeness Criteria (MCC) which is set at 76. This study aims to explain the supporting and inhibiting factors experienced by students and teachers in learning science using madrasa e-learning during the covid-19 pandemic. The research method used in this study is a qualitative method with a case study type. This research was conducted on science learning at the madrasah tsanawiyah level in Indramayu districts. Respondents involved in this study were 458 class VIII students and five madrasah tsanawiyah teachers who were selected using two sampling techniques, namely cluster random sampling and purposive sampling. The supporting instruments in this study were observation sheets and interview sheets. Observation sheets were used to collect data related to the use of e-learning madrasah in science learning. Meanwhile, the interview sheets were used to obtain information about respondents' experiences in using e-learning madrasah and to digest information about the observed phenomena. The results showed that the use of e-learning madrasah in science learning had inhibiting and supporting factors including IT (Information and Technology) capabilities, adequate equipment, learning methods and modes, characteristics of teaching materials, network availability, parental support, and training on the use of e-learning madrasah for students. These seven things act as supporting factors and inhibiting factors that support the characteristics of each school.

Neural network state observer-based robust adaptive iterative learning output feedback control for the rigid-flexible coupled robotic systems with unknown delays and backlash-like hysteresis

Neural network state observer-based robust adaptive iterative learning output feedback control for the rigid-flexible coupled robotic systems with unknown delays and backlash-like hysteresis

Automatic diagnosis of severity of COVID-19 patients using an ensemble of transfer learning models with convolutional neural networks in CT images

Abstract Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT. Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists. Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy. Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.

The Effect of Utilizing Macromedia Flash as a Teaching Media for Delivering Integer on The Learning Outcomes of The Seventh Graders of Mts Miftahul Ulum Pelangwot

This study aimed to see the significant effect of utilizing Macromedia Flash as a teaching media for delivering integer on the learning output of the seventh graders of MTs Miftahu Ulum Pelangwot. It was an experimental research with a control design of one group pre-test post-test design. Hence, it used pre-test and post-test questions as the research instrument. Some tests including validity test, reliability test, and hypothesis test using Paired Sample T-test were all applied for data analysis. The research instrument was validated by an expert validator who confirmed that it was proper to use. The significant result of normality test was 0,2, which was more than 0,05, indicating that the data was normally distributed. The result of Paired Sample Test was tcount > ttable referring to 5,950>2,160. Therefore, it concluded that the significant effect of utilizing Macromedia Flash as a teaching media for delivering integer on the learning outcomes of the seventh graders of MTs Miftahul Ulum Pelangwot obviously existed.

Supervised machine learning application of lithofacies classification for a hydrodynamically complex gas - condensate reservoir in Nam Con Son basin

Conventional integration of rock physics and seismic inversion can quantitatively evaluate and contrast reservoir properties. However, the available output attributes are occasionally not a perfect indicator for specific information such as lithology or fluid saturation due to technology constraints. Each attribute commonly exhibits a combination of geological characteristics that could lead to subjective interpretations and provides only qualitative results. Meanwhile, machine learning (ML) is emerging as an independent interpreter to synthesise all parameters simultaneously, mitigate the uncertainty of biased cut-off, and objectively classify lithofacies on the accuracy scale.
 In this paper, multiple classification algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), K-nearest neighbours (KNN), logistic regression, Gaussian, Bernoulli, multinomial Naïve Bayes, and linear discriminant analysis were executed on the seismic attributes for lithofacies prediction. Initially, all data points of five seismic attributes of acoustic impedance, Lambda-Rho, Mu-Rho, density (ρ), and compressional wave to shear wave velocity (VpVs) within 25-metre radius and 25-metre interval offset top and base of reservoir were orbitally extracted on 4 wells to create the datasets. Cross-validation and grid search were also implemented on the best four algorithms to optimise the hyper-parameters for each algorithm and avoid overfitting during training. Finally, confusion matrix and accuracy scores were exploited to determine the ultimate model for discrete lithofacies prediction. The machine learning models were applied to predict lithofacies for a complex reservoir in an area of 163 km2.
 From the perspective of classification, the random forest method achieved the highest accuracy score of 0.907 compared to support vector machine (0.896), K-nearest neighbours (0.895), and decision tree (0.892). At well locations, the correlation factor was excellent with 0.88 for random forest results versus sand thickness. In terms of sand and shale distribution, the machine learning outputs demonstrated geologically reasonable results, even in undrilled regions and reservoir boundary areas.

Target-driven obstacle avoidance algorithm based on DDPG for connected autonomous vehicles

In the field of autonomous driving, obstacle avoidance is of great significance for safe driving. At present, in addition to traditional obstacle avoidance algorithms including VFH algorithm, artificial potential field method, a large number of related researches are focused on algorithms based on vision and neural networks. Researches on these algorithms have achieved some results, and some of which have completed real road tests. However, most of algorithms consider only local environmental information which may cause local optimum in complex driving environments. Therefore, it is necessary to consider the environmental information beyond the sensor's perceptual ability for autonomous driving in complex environment. In the network-assisted automated driving system, networked vehicles can obtain road obstacles’ and condition information through roadside sensors and mobile network, so as to gain extra sensing ability. Therefore, network-assisted automated driving is of great significance in obstacle avoidance. Under this background, this paper presents an automatic driving obstacle avoidance strategy combining path planning and reinforcement learning. At first, a global optimal path is planned through global information, then merge the global optimal path and vehicle information into a vector. Use this vector as input of reinforcement learning neural network and output vehicle control signals to follow optimal path while avoiding obstacles.

An Advanced Amalgam of Nature-Inspired Algorithms for Global Optimization Problems

Large-scale global optimization problems are ambitious and quite difficult to handle with deterministic methods. The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature. The Grey wolf optimizer (GWO) and teaching-learning-based optimization are the most recently developed and well-known NIAs. GWO is based on the preying strategies of grey wolves while TLBO is based on the effect of the influence of a teacher on the output of learners in a class. NIAs are quite often stuck in the local basins of attraction due to the improper balancing of exploration versus exploitation. In this paper, an advanced amalgam of nature-inspired algorithms (ANIA) is developed by employing GWO and TLBO as constituent algorithms. Initially, an equal number of solutions are assigned to both NIAs to perform their search process of population evolution; then, in later iterations, the number of solutions are allocated to each constituent algorithm based on their individual performance and achievements gained by each algorithm in the previous iteration. The performance of an algorithm is determined at the end of iteration by calculating the ratio of total updated solutions to the total assigned solutions in the amalgam. The proposed strategy effectively balanced the exploration versus exploitation dilemma via compelling the parent algorithms to show continuous improvement during the whole course of the optimization process. The performance of the proposed algorithm, ANIA is evaluated on recently designed benchmark functions of large-scale global optimization problems. The approximated results found by the proposed algorithm are promising as compared to state-of-the-art evolutionary algorithms including the GWO and TLBO in terms of diversity and proximity. The proposed ANIA has tackled most of the benchmark functions efficiently in the parlance of evolutionary computing communities.

ANALISIS PEMBELAJARAN PAI PADA SEKOLAH UMUM

This article aims to find out how PAI learning is carried out in public schools and the limitations or topics studied both in public schools and madrasas. In this case, the author generally reviews the curriculum, syllabus, and textbooks related to PAI learning and the portion of Islamic religious education in public schools. Based on the results of the study, it is known that there are indeed differences in the application of PAI learning between madrasas and public schools. In public schools, PAI is taught in a limited time, which is only allocated 3 hours of lessons. Especially in Aceh, due to the implementation of qanun on local content, lessons that contain religion in public schools get an additional two hours of lessons. So that previously only 3 hours of lessons have now become 5 hours of lessons, especially in SMP/SMPLB. Although the curriculum contains the same material and objectives, if the portion of PAI learning in public schools is very small, it will certainly have an impact on the output of PAI learning to be achieved in public schools.

Mathematics self-belief comparison and examination of pre-service teacher (PST) through a flipped-open calculation based on numbers (ABN) learning method

Teaching mathematics in higher education has been followed traditionally by teacher-centered methodology. With this methodology, there are always certain difficulties in mathematics learning, revealing students are not capable of dealing properly with calculations and/or problems. This research presents the comparison and examination of two different instruction methodologies, traditional-classroom methodology (TCM) and flipped-classroom methodology (FCM) about self-belief of pre-service teacher (PST) in a mathematics course. Here, an open calculation based on numbers (ABN) method originally from the Spanish teaching methodology (abierto basado en números) was used in both teaching methodologies. The 274 PSTs participated in the course: 131 students to T-ABN and 143 students to F-ABN. The results after applying the questionnaire (Cronbach's α = 0.897) showed F-ABN had significant influences in PSTs' self-belief toward the course and made classes more interactive based on parametric-statistical tests. PSTs' self-belief for four studied groups (pre- and post-test in control and experimental group) indicated significant differences found by ANOVA test. Additionally, significant differences were only observed in educational background not in gender of PSTs' self-belief according to factorial ANVOA. In all cases, when significant differences were found, effect size and post-hoc tests were conducted. Therefore, more student-centered methodology allowed to build positive teaching/learning environment for PSTs as future teachers, which consequently expects better learning outputs for children as their future students.

Foreign Learning Environment Reduces Negative Transfer in Pronunciation: An Analysis of the Oral Output of Chinese University EFL Learners

Negative interlingual transfer is a crucial topic in the field of linguistics, which makes the theory of second language acquisition complete. Language transfer refers to transfer of language features between or within languages, in which interlingual transfer is the one focuses on the transfer between two languages. The study explores the typical types of negative interlingual transfer in pronunciation of Chinese university EFL learners and factors affecting the degree of negative interlingual transfer in pronunciation in their L2 learning process. To achieve this purpose, the article adopts error analysis (EA) to scrutinize the errors collected by semi-structured interviews. The participants are 20 Chinese university EFL learners whose L1 is Chinese. Ten of them are from Chinese domestic universities, while the other ten come from Sino-foreign university in China or studying abroad. The result is that: typical errors are divided into segmental errors including mispronouncing vowels, adding a vowel between consonant clusters, wrongly emphasizing and extending ending consonants and replacing consonants in English with similar Chinese phonemes, and suprasegmental mistakes, encompassing stress transfer, too flat or too exaggerated intonation and lack of liaison. As for the contributing factors of negative interlingual transfer, the article contends that learning environment is a significant factor. Foreign learning environment can lead to fewer errors and reduce negative interlingual transfer.

A continuous‐score occupancy model that incorporates uncertain machine learning output from autonomous biodiversity surveys

Abstract Ecologists often study biodiversity by evaluating species occupancy and the relationship between occupancy and other covariates. Occupancy models are now widely used to account for false absences in field surveys and to reduce bias in estimates of covariate relationships. Existing occupancy models take as inputs binary detection/non‐detection observations of species at each visit to each site. However, autonomous sensing devices and machine learning models are increasingly used to survey biodiversity, generating a new type of observation record (i.e. continuous‐score data) that reflects the model's confidence a species is present in each autonomously sensed file, instead of binary detection/non‐detection data. These data are not directly compatible with traditional binary occupancy modelling methods. Here, we develop a new occupancy model that models continuous scores on a visit level as a Gaussian mixture, combining a distribution of scores for files that do contain the species of interest and a distribution of scores for files that do not. The model takes as input continuous scores for each autonomously sensed and classified file, along with an optional small number of binary, manually verified detection and non‐detection annotations. We present a simulation study that shows that over a range of empirically realistic parameters, our model outperforms traditional occupancy models that are based on binary annotation alone. We also apply this new model to an empirical case study using data generated from five machine learning classifiers applied to autonomous acoustic recordings gathered in the eastern United States. Because our occupancy model generalizes allowable input data beyond binary observations, it is particularly well‐suited to the increasing volume of machine learning classified data in ecology and conservation.

A Research on Different Classroom Questioning Strategies Between Chinese and Foreign EFL Teachers

The application of questioning as a teaching tool has always been considered as a main part of the teaching process. Based on the viewpoint of several researchers, a survey was conducted on the application of different questioning strategies by Chinese and foreign EFL teachers in a middle school, in order to determine their effects on students’ language learning output and propose measures to optimize Chinese teachers’ questioning strategies for improving students’ learning efficiency. This research provides preliminary results based on the data collected from the video recordings of class sessions, a questionnaire, and an interview. According to the analysis, the questioning strategies differ between Chinese and foreign EFL teachers in many aspects, such as in information seeking, offering, modification, adjustment, feedback, and so forth.

An Approach to Oral English Assessment Based on Intelligent Computing Model

“English craze” has become a key topic of concern to the majority of the people. Apart from setting English as a compulsory course like Chinese and Mathematics in schools, various English training institutions outside the school are also emerging one after another. Due to the English teaching mode in class, dumb English appears. In recent years, with the popularity of virtual electronic devices, more and more researchers try to use virtual reality (VR) to create an immersive English learning environment. Oral English teaching is an important part of the whole English teaching. In the traditional English classroom teaching practice, teachers’ pronunciation is not standard, and students are difficult to learn the correct pronunciation standard, which makes oral English very passive. The most important problem in oral English teaching is to improve students’ interest in oral English, make students willing to speak and realize English communication. Oral English teaching is an important link in both primary and secondary schools and universities. Teachers are afraid of nonstandard pronunciation in limited classrooms, and they are afraid to speak and unwilling to speak, which leads to passive oral English teaching. Therefore, this paper will set up an intelligent computing model to evaluate and analyze spoken English in a standard and accurate way. Artificial intelligence speech synthesis and imitation of voice change are typical applications of decoupling representation learning in speech, the oral evaluation is based on the proposition that speech is a dynamic and complex process. With the help of the rapidly developed computer speech synthesis and imitation technology, an oral evaluation path based on speech synthesis and imitation is proposed, that is, oral evaluation is carried out by using the network parameters and output of deep learning of computer speech imitation.

Evaluating the Effectiveness of Islamic Religious Education Learning in Implementing 2013 Curriculum in Madrasah

This study aims to obtain objective information, process quality, and output quality in Islamic religious education learning. The present study used a qualitative approach with the model developed by Widoyoko covering the quality of the process and the quality of the learning output. This research applies a program evaluation study method. The data were collected through document studies, interviews, questionnaires, and observations. The results showed that the evaluation of the Islamic Religious Education learning program in Madrasah Aliyah Negeri 1 Central Maluku Regency was in good category (84%) based on the quality of the learning program process and learning output. There are several aspects in evaluating the quality of the process and the quality of learning output that need to be improved. Learning facilities, student motivation, class climate, student attitudes, and students' personal skills should be improved to achieve effective and efficient Islamic religious education learning.

Neural network state observer-based robust adaptive fault-tolerant quantized iterative learning control for the rigid-flexible coupled robotic systems with unknown time delays

In this work, the neural network state observer-based robust adaptive quantized iterative learning output feedback control (RAQILOFC) is investigated for rigid-flexible coupled robotic systems (RFCRSs) with unknown time delays and actuator faults. To deal with hysteresis quantization and actuator defects, a novel fault-tolerant RAQILOFC is designed first, based only on accessible system output data. Then, using the fault-tolerant RAQILOFC laws in combination with the neural network state observer, the two given angular positions are tracked while concurrently suppressing the flexible vibration. Simultaneously, uncertainties associated with system dynamics and unknown time delays are taken into account while designing controllers for RFCRSs. It is demonstrated that the fault-tolerant RAQILOFC technique would converge to and remain inside a predefined small compact set after a finite number of cycles. Additionally, it is shown that the system signal sequences are bounded in presence of unknown time delays and hysteresis quantization. Finally, a numerical example is conducted to illustrate the proposed neural network state observer-based fault-tolerant RAQILOFC strategy’s efficacy.