Використання Виразів Емоцій На Обличчі Людини В Системах Електронного Навчання

Abstract

Since the personal computer usage and internet bandwidth are increasing, e-learning systems are also widely spreading. Although e-learning has some advantages in terms of information accessibility, time and place flexibility compared to the formal learning, it does not provide enough face-to-face interactivity between an educator and learners. In this study, we are proposing a hybrid information system, which is combining computer vision and machine learning technologies for visual and interactive e-learning systems. The proposed information system detects emotional states of the learners and gives feedback to an educator about their instant and weighted emotional states based on facial expressions. In this way, the educator will be aware of the general emotional state of the virtual classroom and the system will create a formal learning-like interactive environment. Herein, several classification algorithms were applied to learn instant emotional state and the best accuracy rates were obtained using kNN and SVM algorithms.

Educational Data Mining Studies in Turkey: A Systematic Review

“…According to the results in Table 5, the topics studied in EDM are investigation the causes of students' failures (Baran & Kilic, 2015;Birtil, 2011;Ucgun, 2009), text mining (Afacan Adanir, 2019;Akcapinar, 2015;Sohsah et al, 2015), determination of variables that affect attitude (Hark, 2013;Idil et al, 2016), determination of reasons for absenteeism (Dalkilic & Aydin, 2017), estimation of instructor performance (Cifci et al, 2018), determination of familial variables affecting reading skill (Avsar & Yalcin, 2015), prediction of the department that students will prefer (Coskun, 2013), modeling of video navigations (Akcapinar & Bayazit, 2018) and prediction emotional states with facial recognition and (Ayvaz et al, 2017).…”

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

“…The study states that Apriori [23] finds the optimal course for the students to choose. Support Vector Machine [23,25] based E-learning models were found to be accurate with 0.986 F-measure compared to ML (Multilayer Neural Network) and SL (Simple Logistic) models [23]. Naïve Bayes, Random Forest and Hidden Markov model that were deployed to evaluate the accuracy of e-learning system out of which Random Forest Tree gave the optimum results with low error rate of 26.716% and high accuracy of the student evaluation system [24].…”

“…Features pertinent to Individual in different study are Userperception, user-opinion [20], Student's choice of course [21] [22], End user(Student) performance and end-user knowledge [23,[24], Learner's facial expression [25].…”

“…We surveyed the existing elearning researches addressing the Course features and the research approaches suitable to handle these features in elearning models. Features pertinent to course in different study are session-likeliness of the course [25] Student-Learning style, Student thinking pattern [26].…”

SSN_NLP at SemEval-2019 Task 3: Contextual Emotion Identification from Textual Conversation using Seq2Seq Deep Neural Network

“…Emotion identification is a process of identifying the emotions automatically from different modalities. Several research work have been presented on detecting emotions from text (Rao, 2016;Abdul-Mageed and Ungar, 2017;Samy et al, 2018;Al-Balooshi et al, 2018;Gaind et al, 2019), speech (Arias et al, 2014;Amer et al, 2014;Lim et al, 2016), images (Shan et al, 2009;Ko, 2018;Ayvaz et al, 2017;Faria et al, 2017;Mohammadpour et al, 2017) and video (Matsuda et al, 2018;Hossain and Muhammad, 2019;Kahou et al, 2016). Emotion understanding from video may be easier by analyzing the body language, speech variations and facial expressions.…”