A Proportional Sentiment Analysis of MOOCs Course Reviews Using Supervised Learning Algorithms

Abstract

When anyone is looking to enroll for a freely available online course so the first and famous name comes in front of the searcher is MOOC courses. So here in this article our focus is to collect the comments by enrolled users for the specified MOOC course and apply sentiment analysis over that data. The significance of our article is to introduce a proficient sentiment analysis algorithm with high perceptive execution in MOOC courses, by seeking after the standards of gathering various supervised learning methods where the performance of various supervised machine learning algorithms in performing sentiment analysis of MOOC data. Some research questions have been addressed on sentiment analysis of MOOC data. For the assessment task, we have investigated a large no of MOOC courses, with the different Supervised Learning methods and calculated accuracy of the data by using parameters such as Precision, Recall and F1 Score. From the results we can conclude that when the bigram model was applied to the logistic regression, the Multilayer Perceptron (MLP) overcomes the accuracy by other algorithms as SVM, Naive Bayes and achieved an accuracy of 92.44 percent. To determine the sentiment polarity of a sentence, the suggested method use term frequency (No of Positive, Negative terms in the text) to calculate the sentiment polarity of the text. We use a logistic regression Function to predict the sentiment classification accuracy of positive and negative comments from the data.

Improvement of Student Interaction Analysis in Online Education Platforms Through Interactive Mobile Technology and Machine Learning Integration

“…With the proliferation and development of online education, student interaction analysis systems on platforms have emerged as pivotal tools for enhancing teaching quality and learning efficiency. Particularly in the realms of massive open online courses (MOOCs) and remote instruction, the optimization of such systems plays a critical role in understanding student behaviors, enhancing engagement, and improving course retention rates [1][2][3][4]. Against this backdrop, the integration of machine learning and computer networking technologies offers new opportunities for optimizing student interaction analysis systems on online education platforms.…”

Analysis of Factors Affecting User Willingness to Use Virtual Online Education Platforms

“…At present, online education platforms have emerged and developed rapidly in response to the proper time and conditions, and the various online education platforms represented by MOOC have been favored by scholars and students [6]. By analyzing the data of online behavior logs of users on the online education platforms, researchers in the computer industry can find out the reasons why users like a type of class or not, and figure out the decisive factors affecting the learning preferences of users, then, the educational resources could be recommended to platform users in a more targeted manner and help them improve learning efficiency [7,8]. The online educa-tion industry has a good outlook, the user number and industry scale both grow fast, attracting widespread attention from various industries [9,10].…”

Online Course Learning Resource Recommendation Based on Difficulty Matching

“…The extensive application of online courses changes the form and content of online course learning resources. Currently, such learning resources are student-oriented, highlighting students' learning engagement and learning enthusiasm [1][2][3][4][5]. During the development of online course learning resources, putting resource diversity over difficulty makes it impossible to grasp the overall learning situation of students [6][7][8][9][10][11][12][13][14].…”