1 Burgas Free University, Faculty of Computer Science and Engineering (BULGARIA)
2 Faculty of Mathematics, Valencia College, Orlando, Florida (UNITED STATES)
About this paper:
Appears in: ICERI2020 Proceedings
Publication year: 2020
Pages: 6346-6350
ISBN: 978-84-09-24232-0
ISSN: 2340-1095
doi: 10.21125/iceri.2020.1365
Conference name: 13th annual International Conference of Education, Research and Innovation
Dates: 9-10 November, 2020
Location: Online Conference
The main goal of the paper is to show the impact of Data Analytics in the field of educational space. The data, accumulated from the work in the educational environments, are constantly increasing. As a result, the data analysis goals can vary: monitoring, forecasting, individualization, intervention in the learning process, assessment and recommendations of the learner and feedback, etc. This paper discusses various analysis techniques: Predictive Analytics (opportunities to predict students' academic performance); Adaptive Analytics (provides the most appropriate level and guides the learner during the learning process), Social Network Analytics (extracts information about human relationships and connections between learners), Discourse Analytics (analyzes participation in discussions, used documents, comments, etc..).

A specific approach is proposed for user model, applying Excel tools. The modeling is based on the accumulated data from the teaching process of students in Mathematics at Valencia College: (Test 1 Sets), (Test 2 Logic), (Test 3 Geometry), (Test 4 Statistics), (Test 5 Probability), (Final Exam), (Quizzes), (Homework), (Projects) and (Class Activities). A number of characteristics for each student are also analyzed: Age, Gender, New Student Experience, FullTime-PartTime, Student Program.

A series of macros has been created regarding: Format Tables, Calculation of average grade for each evaluated unit, Conditional Formatting, Visualization of data with charts, Creation of 3D clustered column chart for the distribution of the average grade of each component of the assessment. Excel functions are used to summarize the data trends.

The proposed models are based on the authors' experience in teaching different disciplines in two educational institutions: Burgas Free University in Bulgaria and Valencia College in Orlando, Florida, USA. Some problems are analyzed and suggestions for improving the results of education are discussed.
Data Analytics, Data Science, e-learning, Model for Dynamic Decision Making.