Since the 1960s, the world has seen how Information Technology (IT) influences education. In the present era, with the massive development of the Internet, various kinds of IT-assisted learning are popping up like mushrooms in the rainy season. However, no matter how advanced IT-assisted learning has been grown, learning media is still an inseparable part of education. In this study, we specifically present how the use of certain types of learning media correlated with students’ access behaviors and, more importantly, students’ achievement. The result shows that these factors have a positive correlation. In terms of media type influence towards students’ achievement, the media that has the appearance of the lecturer gives better achievement, compared to the media that only has audio, and the media that only consists of text and images.

The advancement of Information Technology alters various aspects of human life, including learning. In the present era, on-line learning facilities are provided by institutions, ranging from formal higher education to open course-ware providers. On-line learning or e-learning is mostly achieved through stored media that widely available. These media take forms in various formats such as text and images, slide that equipped with narration from the lecturer, or a video where the lecturer appears inside the frames. We conducted a research about how students would response to the available learning media. The research was conducted with repetitive measures. Each measurement was a module that divided into three parts, where each part was presented to the student as one out of three media listed above. Hence we had three media types for each module. Each module took one week, and at the next week we gather their responses through evaluation forms. All modules were completed in six consecutive weeks. After all modules were completed, we analyze their responses and found that our samples responded best to the video with the appearance of the instructor/lecturer, then the slide with audio, and finally text and images.

Student feedback is an important evaluation tool for quality improvement. Moreover, in Indonesia's higher education system there is an assessment regulation that puts special attention to the availability of the student feedback system. However, parts of the questionnaire are in the form of descriptive text that requires more effort for analysis. This situation leads to a very tiresome work in case of the number of documents reaches several hundred or even thousands. There were some efforts to apply computer-assisted classification by utilizing machine learning, however, most of them only analyzed English documents. Only a handful that studied the classification of documents in Bahasa Indonesia. In reality, we found some cases where the students used mixed languages while filling the evaluation forms. Therefore, in this study, we expand the application of text classification by using Support Vector Machne (SVM) to cases of student feedback in mixed languages. The model was built computationally and from the test, we get 74% accuracy and 0.46 Kappa value.

Advancements in Information Technology have lead the world to new ways of life including in the education field. Nowadays we have various types of computer and Internet-assisted learning. With the booming of blended learning, here comes the flipped classroom environment, where students are expected to learn even before the conventional class meetings started. In this study, we address the question of how students behave toward various learning materials packaged in 3 types of media: text and images, slide shows with audio narration, and slide shows with the appearance of the lecturer. Based on our samples the findings are surprising: some students never made access before the class; and on the other hand, the text-and-image-based learning materials have the highest number of pre-classroom access.

Nowadays, many people who live in remote islands of Indonesia are still facing difficulties in terms of access to information. In the locations where end-to-end communication is not available, the asynchronous approach can be utilized to send information in the form of digital data. In some areas, we could utilize passenger ships or ferries as physical carriers to deliver digital data to the people in the remote islands which are located at a particular range of distance from the ship’s passing routes. This paper reports the channel performance of long-range WiFi connection oversea at 5 GHz using the real ship’s route at the North Sulawesi province‘s water in Indonesia as a sample scenario. The measurement results showed that the most stable ship-to-shore communication can be achieved in ±15 minutes at the maximum distance between the ship and shore of about 4 km. The maximum channel capacity was 120 Mbps for upload (from ship to shore) and 53 Mbps for download (from shore to ship), which is enough to deliver gigabytes of information to the people at the islands every time the ship passes by.

Support vector machine (SVM) is a known method for supervised learning in sentiment analysis and there are many studies about the use of SVM in classifying the sentiments in lecturer evaluation. SVM has various parameters that can be tuned and kernels that can be chosen to improve the classifier accuracy. However, not all options have been explored. Therefore, in this study we compared the four SVM kernels: radial, linear, polynomial, and sigmoid, to discover how each kernel influences the accuracy of the classifier. To make a proper assessment, we used our labeled dataset of students’ evaluations toward the lecturer. The dataset was split, one for training the classifier, and another one for testing the model. As an addition, we also used several different ratios of the training:testing dataset. The split ratios are 0.5 to 0.95, with the increment factor of 0.05. The dataset was split randomly, hence the splitting-training-testing processes were repeated 1,000 times for each kernel and splitting ratio. Therefore, at the end of the experiment, we got 40,000 accuracy data. Later, we applied statistical methods to see whether the differences are significant. Based on the statistical test, we found that in this particular case, the linear kernel significantly has higher accuracy compared to the other kernels. However, there is a tradeoff, where the results are getting more varied with a higher proportion of data used for training.

Assessment is an integral part of education. Through assessment, instructors may evaluate the learners' achievement. In a large class setup, holding an assessment and checking the learners' work leads to a tiresome job if done manually. Therefore, the use of an automated procedure, such as a computerized solution is advised. However, the question types that can be automatically checked does not cover essay question. It is very unfortunate for mathematical-based courses where evaluating the reasoning of the learners is essential, hence adopting question types such as Multiple Choice Questions, True/False; or even Numeric, which ask the learner to input a single number as an answer, would be inadequate. In this paper, we describe the implementation of the STACK, a MOODLE plugin that supports the assessment of mathematical expressions. The preliminary results show that with the correct way of introducing the commands to the learners, instructors can author questions with adequate discrimination index and efficiency.   Penilaian merupakan bagian integral dari pendidikan. Melalui penilaian, instruktur dapat mengevaluasi prestasi peserta didik. Dalam pengaturan kelas besar, mengadakan penilaian dan memeriksa hasil kerja siswa akan menjadi pekerjaan yang melelahkan jika dilakukan secara manual. Oleh karena itu, disarankan untuk menggunakan prosedur otomatis, seperti solusi terkomputerisasi. Namun, jenis soal yang dapat diperiksa secara otomatis tidak mencakup soal esai. Sangat disayangkan untuk kursus berbasis matematika di mana mengevaluasi penalaran peserta didik sangat penting, sehingga mengadopsi jenis pertanyaan seperti Pertanyaan Pilihan Ganda, Benar/Salah; atau bahkan Numerik, yang meminta pembelajar untuk memasukkan satu angka sebagai jawaban, tidak akan memadai. Dalam makalah ini, kami menjelaskan implementasi STACK, sebuah plugin MOODLE yang mendukung penilaian ekspresi matematika. Hasil awal menunjukkan bahwa dengan cara yang benar dalam memperkenalkan perintah kepada peserta didik, instruktur dapat menulis pertanyaan dengan indeks pembedaan dan efisiensi yang memadai.

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