Open AccessAnalyzing types of interaction in nuclear magnetic spectroscopy online discussion forums that affects student learning outcomes
Author(s) -
I. W. A. Terra,
Surjani Wonorahardjo,
Sri Suharti
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1465/1/012055
Subject(s) - online discussion , psychology , cognition , coding (social sciences) , qualitative research , mathematics education , qualitative property , chemistry , computer science , world wide web , mathematics , statistics , sociology , social science , neuroscience , machine learning
Many research shows that interaction can increase concept understanding. Course interaction can be categorized in student-student interaction and student-lecturer interaction. Strong interaction can increasing students high order thinking skill. Learning design begins to pay attention to that interaction effect. Interaction can be done online and lead to cognitive understanding. This qualitative descriptive research aims to describe the cognitive process in an online discussion, specifically in analytical chemistry course. They learn about nuclear magnetic spectroscopy that most used in both qualitative and quantitative analytical chemistry. The subjects are seven groups consists of three chemistry students in their third year. They have three worksheets to discuss in three weeks about basic principal, instrument, spectrum, and experimental design in Nuclear Magnetic Resonance spectroscopy. Data sources are their online discussion transcript obtained from their elearning discussion forum. They also have pretest and posttest to analyze N-gain score. Their discussions are analyzed using coding categories to count the number of meaningful interaction. The trend of n-gain score and number of meaningful interaction are analyzed. Type of interaction that showed is ARA, ARC, ARI, DRA, DRI, and DRC. Results show that gain score correlates with number of meaningful interactions.