Impact of E-Learning Media on Students’ Critical Thinking Skills at Physics Education Study Program, Almuslim University

Starting from the emergence of student boredom in taking part in the course of recovery, resulting in decreased thinking skills of students who focus more on memorizing concepts instead of understanding concepts. The researcher combines the learning model assisted by e-learning which is currently of interest. The learning model assisted by e-learning media combines principles in the learning process and technology. This study aims to determine the impact of learning with e-learning media on the improvement of critical thinking skills of students in semester III of the physics education study program. Data collection for this research was carried out in September-October 2019, this research is also the impact of the accompaniment of the PDS Grant in won by FKIP Umuslim in the 2019 budget year. Data analysis in this study used a parametric statistical test, namely the one-tail t-test. The results showed that the use of learning models assisted by e-learning media had a very positive impact on improving students' critical thinking skills. This can be seen from the acquisition of N-Gain for each student's critical thinking indicator in the medium category as many as two indicators, namely indicators providing further explanation and indicators of managing strategies, while the other three indicators get high categories, namely indicators providing simple explanations, indicators of building basic skills, and indicators set strategy or tactics

Self-regulated learning in an e-learning environment in a Malaysian University

This study aimed to conceptualise, design and validate an instrument for measuring self-regulated learning in the e-learning environment. It examined how students at Univerisiti Sains Malaysia (USM) self-regulate their learning in an e-learning environment. It investigated how learners monitor their reflections, learning strategies, metacognitive awareness, intrinsic motivation, extrinsic motivation and amotivation in their learning activities.A conceptual model of self-regulated learning in an e-learning environment was developed from a review of pertinent literature. This model was then used to develop a student self-report rating scale instrument, the data from which were scrutinised by the Statistical Package for the Social Sciences (IBM -SPSS), and Rasch Unidimensional Measurement Models (RUMM2030).Quantitative research methodology was adopted based on deductive approach. Thus, convenience sampling was employed for university students who volunteered to participate anonymously.Factor analysis identified 28 factors and after data reduction, eight „natural‟ groupings were found. The factors were Ability and Effort Beliefs, Reflection, Introjected Regulation, Task Character, Strategic Use, Value of Task, Stimulus Response and Recognition. Data from the respective items comprising the eight factors were then analysed using RUMM20303 to ascertain whether the factors could be measured. This showed that measures had been constructed. Data were also examined for the effects of categorical variables such as student gender, age, year of study, ethnicity and school.The findings of this study provide useful information for university instructional technologists, software developers, students, facilitators, administrators and researchers who are interested in self-regulated learning and ways in which information and communication e-learning technology can enhance and facilitate learning. The study is also significant because it used a highly contemporary method for instrument development and data analysis – the Rasch model

An active learning approach for statistical spoken language understanding

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-25085-9_67In general, large amount of segmented and labeled data is needed to estimate statistical language understanding systems. In recent years, different approaches have been proposed to reduce the segmentation and labeling effort by means of unsupervised o semi-supervised learning techniques. We propose an active learning approach to the estimation of statistical language understanding models that involves the transcription, labeling and segmentation of a small amount of data, along with the use of raw data. We use this approach to learn the understanding component of a Spoken Dialog System. Some experiments that show the appropriateness of our approach are also presented.Work partially supported by the Spanish MICINN under contract TIN2008-06856-C05-02, and by the Vicerrectorat d’Investigació, Desenvolupament i Innovació of the Universitat Politècnica de València under contract 20100982.García Granada, F.; Hurtado Oliver, LF.; Sanchís Arnal, E.; Segarra Soriano, E. (2011). An active learning approach for statistical spoken language understanding. En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Verlag (Germany). 7042:565-572. https://doi.org/10.1007/978-3-642-25085-9_67S5655727042De Mori, R., Bechet, F., Hakkani-Tur, D., McTear, M., Riccardi, G., Tur, G.: Spoken language understanding: A survey. IEEE Signal Processing Magazine 25(3), 50–58 (2008)Fraser, M., Gilbert, G.: Simulating speech systems. Computer Speech and Language 5, 81–99 (1991)Gotab, P., Bechet, F., Damnati, G.: Active learning for rule-based and corpus-based spoken labguage understanding moldes. In: IEEE Workshop Automatic Speech Recognition and Understanding (ASRU 2009), pp. 444–449 (2009)Gotab, P., Damnati, G., Becher, F., Delphin-Poulat, L.: Online slu model adaptation with a partial oracle. In: Proc. of InterSpeech 2010, Makuhari, Chiba, Japan, pp. 2862–2865 (2010)He, Y., Young, S.: Spoken language understanding using the hidden vector state model. Speech Communication 48, 262–275 (2006)Ortega, L., Galiano, I., Hurtado, L.F., Sanchis, E., Segarra, E.: A statistical segment-based approach for spoken language understanding. In: Proc. of InterSpeech 2010, Makuhari, Chiba, Japan, pp. 1836–1839 (2010)Riccardi, G., Hakkani-Tur, D.: Active learning: theory and applications to automatic speech recognition. IEEE Transactions on Speech and Audio Processing 13(4), 504–511 (2005)Segarra, E., Sanchis, E., Galiano, M., García, F., Hurtado, L.: Extracting Semantic Information Through Automatic Learning Techniques. International Journal of Pattern Recognition and Artificial Intelligence 16(3), 301–307 (2002)Tur, G., Hakkani-Tr, D., Schapire, R.E.: Combining active and semi-supervised learning for spoken language understanding. Speech Communication 45, 171–186 (2005

Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control

Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. In this work, we leverage the statistical approach of bootstrap aggregating (bagging) to robustify the sparse identification of nonlinear dynamics (SINDy) algorithm. First, an ensemble of SINDy models is identified from subsets of limited and noisy data. The aggregate model statistics are then used to produce inclusion probabilities of the candidate functions, which enables uncertainty quantification and probabilistic forecasts. We apply this ensemble-SINDy (E-SINDy) algorithm to several synthetic and real-world data sets and demonstrate substantial improvements to the accuracy and robustness of model discovery from extremely noisy and limited data. For example, E-SINDy uncovers partial differential equations models from data with more than twice as much measurement noise as has been previously reported. Similarly, E-SINDy learns the Lotka Volterra dynamics from remarkably limited data of yearly lynx and hare pelts collected from 1900-1920. E-SINDy is computationally efficient, with similar scaling as standard SINDy. Finally, we show that ensemble statistics from E-SINDy can be exploited for active learning and improved model predictive control

The Development of Web-based Graphical User Interface for Learning and Fitting Generalized Estimating Equation with Spline Smoothers

Statistical modeling (regression analyses) have been growing rapidly into various directions to accommodate various data conditions. For longitudinal or repeated measures data, one of the suitable models is GEE (Generalized Estimating Equation). In practice, to do complex modeling such as GEE, the use of statistical software is necessary and it is available on free open source software R. However, GEE modeling on R can only be access through command line interface (CLI), and most practical researchers very much rely on Graphical User Interface (GUI) based statistics software. To make access to GEE (both order 1 and 2) much easier, we developed, using Shiny toolkit, two types of web-based GUI, standard pull down menu type and e-module type (with narrative theories) that can be utilized for learning and fitting GEE. This paper discusses the features of the interfaces and illustrates the use of them. Keywords: longitudinal data, Generalized Estimating Equation (GEE), exponential families, statistical modeling, correlated response, nonparametric, natural splines, shiny toolki

DEVELOPING E-LEARNING ADAPTIVE TUTORIAL MODEL TO OPTIMIZE IN-SERVICE AND PRE-SERVICE TEACHERS’ PRFFESSIONAL AND PEDAGOGICAL PHYSICS

Penelitian ini bertujuan untuk menghasilkan model tutorial e-learning adaptif untuk meningkatkan kompetensi profesional dan kompetensi pedagogi guru fisika pada topik listrik magnet. Metode penelitian yang digunakan adalah metode penelitian dan pengembangan (R and D). Pengembangan model tutorial e-learning adaptif berdasarkan pada tingkat pengetahuan peserta didik dan sistem pembelajaran tuntas. Kajian difokuskan pada pengembangan konten adaptif, pengembangan sistem e-learning adaptif dan pengembangan kegiatan tutorial dengan menggunakan model tutorial e-learning adaptif, karakteristik model tutorial e-learning adaptif, keefektivan penggunaan model tutorial e-learning adaptif untuk meningkatkan kompetensi profesional dan kompetensi pedagogi, tanggapan peserta didik terhadap model tutorial e-learning adaptif, dan kekuatan dan kelemahan penerapan model tutotial e-learning adaptif. Data dikumpulkan melalui uji keterpahaman, tes kompetensi profesional, tes kompetensi pedagogi, dan kuesioner tentang respon peserta didik terhadap model tutorial e-learning adaptif. Analisis data dilakukkan melalui analisis gain yang dinormalisasikan sedangkan analisis data non-tes menggunakan persentase. Model tutorial e-learning adaptif memiliki karakteristik yang menitikberatkan pada pengembangan kompetensi guru dengan mempertimbangkan latar belakang peserta. Guru dengan kemampuan yang beragam dilatih dengan menggunakan tutor dalam bentuk video interaktif yang disajikan pada model e-learning adaptif. Sistem e-learning menyajikan materi, mengajukan pertanyaan, menyediakan alat untuk kegiatan lab virtual dan umpan balik terhadap hasil tes yang diberikan. Setiap guru harus menyelesaikan semua tugas yang diberikan oleh sistem supaya dapat mengikuti materi selanjutnya. Penelitian ini dilakukan di Kota Palembang dengan melibatkan 20 guru fisika inservice dan 50 guru fisika preservice. Peningkatan kompetensi profesional guru inservice untuk kelompok pemula dan ahli dengan sebesar 0,42 dan 0,45 sedangkan kompetensi pedagogi sebesar 0,63 dan 0,65. Semua dengan katagori sedang. Hasil analisis kompetensi profesional dan pedagogi guru mununjukkan bahwa tidak terdapat perbedaan pengaruh penggunaan e-learning adaptif terhadap kompetensi profesional dan pedagogi guru fisika inservice dengan kata lain e-learning adaptif keduanya dapat meningkatkan kompetensi profesional dan pedagogi guru fisika inservice. Untuk melihat keefektivan e-learning adaptif dilakukan ujicoba pada guru fisika preservice. Hasil uji statistik dengan menggunakan Anova menunjukkan bahwa model tutorial e-learning adaptif secara efektif meningkatkan kompetensi profesional dan kompetensi pedagogi guru fisika. Hasil analisis data angket menunjukkan bahwa peserta didik memberikan tanggapan positif terhadap model tutorial e-learning adaptif untuk meningkatkan kompetensi profesional dan kompetensi pedagogi guru fisika;---This study aims to develop a tutorial model using adaptive e-learning to improve the professional skills and teaching competence of physics teachers on the subject of magnetic electricity. The research method used is research and development (R & D). The Development of adaptive online learning tutorial models was related with students' level of knowledge and comprehensive learning systems. The study was focused at developing adaptive content, adaptive e-learning systems and didactic activities using adaptive didactic models, the characteristics of e-learning didactic models adaptive competence, students' responses to adaptive online learning tutorial model and the strengths and weaknesses of applying adaptive online learning tutorial models. Data was collected through comprehension tests, professional skill tests, pedagogical skills tests, and questionnaires on student responses to adaptive online learning tutorial models. Data analysis is performed using standardized gain analysis , while analysis of untested data uses percentages. The adaptive online learning tutorial model has characteristics that focus on developing teachers 'skills, taking into account participants' backgrounds. Skilled teachers are trained with tutors in the form of interactive videos presented in the adaptive e-learning model. The eLearning system presents the material, asks questions, provides tools for virtual lab activities and comments on the test results provided. Each teacher must perform all the tasks given by the system in order to follow the following material. This research was conducted in the city of Palembang with the participation of 20 in-service physics teachers and 50 physics teachers on duty. The improvement of the professional competence of teachers in initial training for beginners and experts is 0.42 and 0.45 while the pedagogical competence is 0.63 and 0.65. All is with the middle category. The results of the analysis of teachers' professional competence and pedagogy showed that there was no difference between the use of adaptive e-learning on professional skills and the physics teacher in pedagogic service. In other words, adaptive e-learning can both improve professional competence and inservice physics teacher pedagogy. To see the effectiveness of adaptive e-learning, a test was conducted on reserve physics teachers. The results of statistical tests using Anova show that the adaptive e-learning tutorial model effectively improves the professional competence and pedagogical competence of physics teachers. The results of the analysis of the questionnaire data showed that students gave a positive response to the adaptive online learning tutorial model to improve the professional competence and pedagogical competence of physics teachers