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Emotion Detection and Opinion Mining from Student Comments for Teaching Innovation Assessment
<!DOCTYPE html> <html> <head> <title>Emotion Detection and Opinion Mining from Student Comments for Teaching Innovation Assessment</title> <!-- common meta tags --> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="Emotion Detection and Opinion Mining from Student Comments for Teaching Innovation Assessment"> <meta name="description" content="Students can provide their opinions, comments, or suggestions about a course, course instructor, study environment, and available resources using the course evaluation at the end of every semester. This helps the course professors and other college authorities make appropriate changes or continue a particular approach to get the best experience in classrooms. These course evaluations are in both quantitative and qualitative forms. In quantitative feedback the evaluation is performed in terms of measurable outcomes and include a Likert-type scale to capture the level of agreement and disagreement. In qualitative feedback the students can convey their feelings, opinions or suggestions about the course, the course instructor, or their overall thoughts/comments towards the course. The qualitative feedbacks provide freedom for the students to express their honest thoughts on a course. The data collected in the qualitative form provides deeper insight into a student鈥檚 emotional state. In this work we focus on mining the qualitative student feedbacks and analyzing the student sentiments. We also analyze the efficiency of Light Weight teams and Flipped Classroom approach which are Active Learning methods. Results show that the implementation of these Active Learning methods is linked with increased positivity in student emotions"/> <meta name="keywords" content="Sentimental Analysis, Qualitative Feedback, Active Learning Approach, Emotion Mining, Flipped Classroom, Light Weight Teams, Emotion Detection, Opinion Mining"/> <!-- end common meta tags --> <link rel="icon" type="image/ico" href="/ico.ico"/> <link rel="stylesheet" type="text/css" href="../main.css" /> <style> ul li a{ font-weight:bold; color:#000; list-style:none; text-decoration:none; size:10px;} .imagess { height:90px; text-align:left; margin:0px 5px 2px 8px; float:right; border:none; } a { text-decoration:none; } #left p { font-family:CALIBRI; font-size:0.90pc; margin-left: 20px; } .right { margin-right: 20px; } #button{ float: left; font-size: 17px; margin-left: 10px; height: 28px; width: 100px; background-color: #1e86c6; } </style> </head> <body> <div id="wap"> <div id="page"> <div id="top"> <table width="100%" cellspacing="0" cellpadding="0" > <tr><td colspan="3" valign="top"><img src="../ije.jpg"/></td></tr> </table> </div> <div id="menu"> <a href="../index.html">Home</a> <a href="../editorial.html">Editorial</a> <a href="../paper.html">Submission</a> <a href="../indexing.html">Indexing</a> <a href="../specialissue.html">Special Issue</a> <a href="../contact.html">Contacts</a> <a href="http://airccse.com" target="_blank">AIRCC</a></div> <div id="content"> <div id="left"> <h2>Volume 9, Number 2</h2> <h4 style="text-align:center;height:auto;color:white"><a>Emotion Detection and Opinion Mining from Student Comments for Teaching Innovation Assessment</a></h4> <h3> Authors</h3> <p class="#left">Angelina Tzacheva and Akshaya Easwaran, University of North Carolina at Charlotte, USA</p> <h3> Abstract</h3> <p class="#left right" style="text-align:justify">Students can provide their opinions, comments, or suggestions about a course, course instructor, study environment, and available resources using the course evaluation at the end of every semester. This helps the course professors and other college authorities make appropriate changes or continue a particular approach to get the best experience in classrooms. These course evaluations are in both quantitative and qualitative forms. In quantitative feedback the evaluation is performed in terms of measurable outcomes and include a Likert-type scale to capture the level of agreement and disagreement. In qualitative feedback the students can convey their feelings, opinions or suggestions about the course, the course instructor, or their overall thoughts/comments towards the course. The qualitative feedbacks provide freedom for the students to express their honest thoughts on a course. The data collected in the qualitative form provides deeper insight into a student鈥檚 emotional state. In this work we focus on mining the qualitative student feedbacks and analyzing the student sentiments. We also analyze the efficiency of Light Weight teams and Flipped Classroom approach which are Active Learning methods. Results show that the implementation of these Active Learning methods is linked with increased positivity in student emotions. </p> <h3> Keywords</h3> <p class="#left right" style="text-align:justify">Sentimental Analysis, Qualitative Feedback, Active Learning Approach, Emotion Mining, Flipped Classroom, Light Weight Teams, Emotion Detection, Opinion Mining. </p><br> <button type="button" id="button"><a target="blank" href="/ije/papers/9221ije03.pdf">Full Text</a></button> <button type="button" id="button"><a href="../current2021.html">Volume 9</a></button> <br><br><br><br><br> </div> <div id="right"> <div class="menu_right"> <ul><li><a href="../index.html">Scope & Topics</a></li> <li><a href="/ethics.html" target="blank">Ethics</a></li> <li ><a href="../archives.html">Archives</a></li> <li><a href="../paper.html">Paper Submission</a></li> <li><a href="/faq.html" target="blank">FAQ</a></li> </ul> </div><br /> <p align="center"> </p> <p align="center"> </p> </div> <div class="clear"></div> <div id="footer"><table width="100%" ><tr><td height="25" colspan="2"><br /><p align="center">© AIRCC Publishing Corporation</p></td></tr></table> </div> </div> </div> </div> </body> </html>