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.
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.
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.
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.
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