Aggrawal, S. (2023, October), Literature Review of Analyzing and Predicting Students’ Performance in Examinations    Paper presented at 2023 Fall Mid Atlantic Conference: Meeting our students where they are and getting them where they need to be, Ewing, New Jersey. 10.18260/1-2--45126

\bibitem{asee_peer_45126}
  Aggrawal, S. (2023, October), \emph{Literature Review of Analyzing and Predicting Students’ Performance in Examinations  }
  Paper presented at 2023 Fall Mid Atlantic Conference: Meeting our students where they are and getting them where they need to be, Ewing, New Jersey.
  10.18260/1-2--45126

Sakhi Aggrawal.  "Literature Review of Analyzing and Predicting Students’ Performance in Examinations  ".  2023 Fall Mid Atlantic Conference: Meeting our students where they are and getting them where they need to be, Ewing, New Jersey, 2023, October.  ASEE Conferences, 2023.  https://peer.asee.org/45126 Internet.  17 May, 2024

\bibitem{asee_peer_45126}
  Sakhi Aggrawal.
  "Literature Review of Analyzing and Predicting Students’ Performance in Examinations  ".
  \emph{2023 Fall Mid Atlantic Conference: Meeting our students where they are and getting them where they need to be, Ewing, New Jersey, 2023, October}.
  ASEE Conferences, 2023.
  https://peer.asee.org/45126 Internet.  17 May, 2024

@INPROCEEDINGS{asee_peer_45126
author = "Sakhi Aggrawal"
title = "Literature Review of Analyzing and Predicting Students’ Performance in Examinations  "
booktitle = "2023 Fall Mid Atlantic Conference: Meeting our students where they are and getting them where they need to be"
year = "2023"
month = "October"
address = "Ewing, New Jersey"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/45126}
number = {10.18260/1-2--45126}
}

TY  - CPAPER
AB  - Student dropout continues to be a critical problem in education. The sooner students at risk of dropping out are identified, the sooner necessary measures can be taken to support and guide them. Schools and universities are implementing data science methods to analyze available data, identify patterns, and extract information to support decision-making and effective student learning. However, student learning is often difficult to assess. Examinations are a popular assessment tool for testing students’ knowledge, skills, and aptitude among others. Student performance in examinations can indicate their risk of dropping out, therefore, it becomes critical to analyze and predict their examination performance. In this literature review, we focus on exploring and analyzing existing work in this field to develop fundamental domain knowledge and avoid duplicative work. The specific research question was: What types of knowledge (i.e., conceptual learning, problem-solving, and model building) already exist in relation to analyzing and predicting students’ performance in examinations? The steps followed for performing this literature review were: (1) identifying the scope and research questions, (2) defining the inclusion and exclusion search criteria of literature, and (3) classifying and cataloging the literature sources that relate to analyzing and predicting students’ performance in examinations. The final data set is comprised of a total of 10 papers that meet our criteria. 
AU  - Sakhi Aggrawal
CY  - Ewing, New Jersey
DA  - 2023/10/27
PB  - ASEE Conferences
TI  - Literature Review of Analyzing and Predicting Students’ Performance in Examinations  
UR  - https://peer.asee.org/45126
DO  - 10.18260/1-2--45126
ER  -