Peningkatan Performa Pengelompokan Siswa Berdasarkan Aktivitas Belajar pada Media Pembelajaran Digital Menggunakan Metode Adaptive Moving Self-Organizing Maps

Digitalisasi proses pembelajaran memungkinkan untuk dihasilkannya rekaman terhadap setiap aktivitas siswa selama belajar. Rekaman yang dihasilkan tersebut dapat digunakan untuk mengelompokkan siswa berdasarkan pola dari proses belajar yang dilakukan. Hasil pengelompokkan yang peroleh dapat digunakan untuk melakukan penyesuaian komponen pembelajaran ataupun metode pembelajaran bagi siswa. Salah satu metode pengelompokan yang sering digunakan adalah Self-Organizing Maps (SOM), SOM merupakan metode jaringan syaraf tiruan dengan tujuan untuk mempertahankan topologi data ketika data input multidimensi diubah menjadi data output dengan dimensi yang lebih rendah. Neuron SOM pada dimensi input diperbaharui sepanjang proses pelatihan, sedangkan neuron pada dimensi output tidak mendapatkan pembaruan sama sekali, hal ini menyebabkan struktur neuron yang digunakan pada tahapan inisialisasi akan tetap sama hingga akhir proses pengelompokan. Pada penelitian ini menggunakan metode Adaptive Moving Self-Organizing Maps (AMSOM) yang menggunakan struktur neuron lebih fleksibel, dengan dimungkinkannya terjadi perpindahan, penambahan dan penghapusan dari neuron menggunakan data 12 assignments dari media pembelajaran MONSAKUN. Hasil penelitian menunjukkan terdapat perbedaan yang signifikan secara statistik antara nilai quantization error dan nilai topographic error dari algoritme AMSOM dengan algoritme SOM. Metode AMSOM menghasilkan rata-rata nilai quantization error 27 kali lebih kecil dan rata-rata nilai topographic error 54 kali lebih kecil dibandingkan dengan metode SOM.AbstractThe digitization of the learning process makes it possible to produce recordings of each student's activity during learning. The resulting record can be used to group students based on the pattern of the learning process. The grouping results can be used to make adjustments to the learning components or learning methods for students. One of the most frequently used clustering methods is Self-Organizing Maps (SOM), SOM is a neural network method to maintain data topology when multidimensional input data is converted into output data with lower dimensions. The SOM neurons in the input dimension are updated throughout the training process, while the neurons in the output dimension do not get updated at all, this causes the neuron structure used in the initialization stage to remain the same until the end of the grouping process. In this study, the Adaptive Moving Self-Organizing Maps (AMSOM) method uses a more flexible neuron structure, allowing for the transfer, addition and deletion of neurons using 12 assignments of data from MONSAKUN learning media. The results showed that there was a statistically significant difference between the quantization error and the topographic error of the AMSOM algorithm and the SOM algorithm. The AMSOM method produces an average quantization error 27 times smaller and an average topographic error 54 times smaller than the SOM method

Development of MONSAKUN Touch and Practical Use in Class: Realization of Lesson of Posing of Arithmetical Word Problems

和若しくは差で解ける算数文章問題を対象とした単文統合型の作問学習支援システム“モンサクン”は数年に渡り複数の小学校の授業で利用されている．しかしながら，これらの授業では既に対象領域の学習を終えている2年生以上を対象としていたため，付加的な学習としての位置づけになっていた．本研究では，対象領域を学習中である1年生を対象に，単文統合型の作問の実施を目指した．このためにはシステムと授業の融合が必要となるため，(1) システムの通常教室での利用，(2) 作問状況のリアルタイムでの把握，(3) 作問法の教授，が解決すべき課題となった．本論文ではこれらの課題を解決するために行った，(I) モンサクンのタブレット化，(II) 作問状況モニタリングと集計・可視化機能の実現，(III) 作問法の教授法の考案，を述べる．またシステムを用いた9時限に渡る実践を行い，その分析結果から，システムが十分に利用可能であったこと，学習者の問題解決能力や作問能力の向上が見られたことが確認できたので，これを報告する

算数文章題の単文統合型作問についてのモデルベースのプロセス分析

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

Multimedia Development of English Vocabulary Learning in Primary School

In this paper, we describe a prototype of web-based intelligent handwriting education system for autonomous learning of Bengali characters. Bengali language is used by more than 211 million people of India and Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. This research project was aimed to develop an intelligent Bengali handwriting education system. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide a feedback to the students to correct themselves. Our proposed system can be accessed from smartphone or iPhone that allows students to do practice their Bengali handwriting at anytime and anywhere. Bengali is a multi-stroke input characters with extremely long cursive shaped where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm for Bengali language learning. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older national. The experimental results showed that our proposed hierarchical recognition algorithm can provide higher accuracy than traditional multi-stroke recognition algorithm with more writing variability

Developing Learning System in Pesantren The Role of ICT

According to Krashen's affective filter hypothesis, students who are highly motivated have a strong sense of self, enter a learning context with a low level of anxiety, and are much more likely to become successful language acquirers than those who do not. Affective factors, such as motivation, attitude, and anxiety, have a direct impact on foreign language acquisition. Horwitz et al. (1986) mentioned that many language learners feel anxious when learning foreign languages. Thus, this study recruits 100 college students to fill out the Foreign Language Classroom Anxiety Scale (FLCAS) to investigate language learning anxiety. Then, this study designs and develops an affective tutoring system (ATS) to conduct an empirical study. The study aims to improve students’ learning interest by recognizing their emotional states during their learning processes and provide adequate feedback. It is expected to enhance learners' motivation and interest via affective instructional design and then improve their learning performance

Integration of Reciprocal Teaching-ICT Model To Improve Students’Mathematics Critical Thinking Ability

This research examines the effectiveness on how mathematics teachers have begun to integrate information and communication technology (ICT) with reciprocal teaching model to improve students’ mathematics critical thinking ability into seventh junior high school classroom practice. This study was experimental research with a quasi-experimental design. The samples of the study are 36 students for classroom experiments and 36 students for classroom control. The instruments employed in this study were pre-test and post-test. All the instruments are made in essays forms. The data were analyzed by using descriptive statistics. Based on the research findings, it was gotten that (1) the development of teaching instructional multimedia of the seven grade students of junior high school; (2) the improvement of students’ mathematics critical thinking ability in experimental class; (3) the aspect of attractiveness shows that the developed instructional multimedia was very interesting; and (4) reciprocal learning has good impact on students’ mathematics critical thinking ability