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A Learning Analytics Approach for Student Performance Assessment


Affiliations
1 Department of Computer Science, Helwan University, Cairo, Egypt
 

Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement.

Keywords

Big Data, Student Success, Performance Indicators.
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  • Bienkowski M, Feng M and Means B 2012 Enhancing teaching and learning through educational data mining and learning analytics: An issue brief Washington, DC SRI Int. 1–57

  • Elias T 2011 Learning Analytics : Definitions , Processes and Potential Learning 23 134–48

  • Romero C and Ventura S 2013 Data mining in education Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3 12–27

  • Hung J, Hsu Y-C and Rice K 2012 Integrating Data Mining in Program Evaluation of K-12 Online Education Educ. Technol. Soc. 15 27–41

  • O B L I N G E R By G, Campbell J P, Deblois P B and Oblinger D G 2007 “Academic Analytics: A New Tool for a New Era”

  • EDUCAUSE 2010 Next generation learning challenges: Learner analytics premises Educause

  • Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood K 2011 The horizon report: 2011 edition

  • Reyes J A 2015 The Skinny on Big Data in Education: Learning Analytics Simplified. TechTrends: Linking Research and Practice to Improve Learning 59(2), 75-80

  • Jayaprakash S M, Moody E W, Lauría E J M, Regan J R and Baron J D 2014 Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative J. Learn. Anal. 1 6–47

  • Koedinger K R, Baker R S J, Cunningham K and Skogsholm A 2010 A Data Repository for the EDM community : The PSLC DataShop Handb. Educ. Data Min. 43–55

  • Gaševic D, Dawson S and Siemens G 2015 Let ’ s not forget : Learning Analytics are about Learning Course Signals : Lessons Learned TechTrends71–64 59

  • Dietz-Uhler B and Hurn J 2013 Using learning analytics to predict (and improve) student success: a faculty perspective J. Interact. Online Learn. 12 17–26

  • Johnson L, Adams Becker S, Estrada V, Freeman A, Kampylis P, Vuorikari R and Punie Y 2014 Horizon Report Europe: 2014 Schools Edition_Sintese e Introdução PT

  • Hadwin A F, Nesbit J C, Jamieson-Noel D, Code J and Winne P H 2007 Examining trace data to explore self-regulated learning Metacognition Learn. 2 107–24

  • Blikstein P 2011 Using learning analytics to assess students’ behavior in open-ended programming tasks Proc. 1st Int. Conf. Learn. Anal. Knowl. - LAK ’11 110

  • Fan X and Chen M 2001 Parental involvement and students’ academic achievement: A metaanalysis Educ. Psychol. Rev. 13 1–22

  • Agnihotri L and Ott A 2014 Building a Student At-Risk Model : An End-to-End Perspective Proc.

  • th Int. Conf. Educ. Data Min. 209–12

  • Minaei-Bidgoli B, Kashy D a., Kortemeyer G and Punch W F 2003 Predicting student performance: an application of data mining methods with an educational web-based system 33rd Annu. Front. Educ. 2003. FIE 2003. 1T2A_13-T2A_18

  • Dringus L P and Ellis T 2005 Using data mining as a strategy for assessing asynchronous discussion forums Comput. Educ. 45 141–60

  • Morris L V, Wu S S and Finnegan C L 2005 Predicting Retention In Online General Education Courses Am. J. Distance Educ. 19 23–36

  • Arnold K E, Pistilli M D and Arnold K E 2012 Course Signals at Purdue: Using Learning Analytics to Increase Student Success 2nd Int. Conf. Learn. Anal. Knowl. 2–5 [22] John Whitmer K F and W A 2012 Analytics in Progress: Technology Use, Student Characteristics, and Student Achievement

  • Gašević D, Dawson S, Rogers T and Gasevic D 2016 Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success Internet High. Educ. 28 68–84

  • McAfee A, Brynjolfsson E, Boyd D, Crawford K and Lohr S 2012 Critical Questions for Big Data Information, Commun. Soc. 15 1–5

  • Shum S B and Ferguson R 2012 Social Learning Analytics Educ. Technol. Soc. 15 3–26

  • Siemens G and Long P 2011 Penetrating the Fog: Analytics in Learning and Education Educ. Rev. 46 30–2

  • Morris L V, Finnegan C and Wu S 2005 Tracking student behavior , persistence , and achievement in online courses 8 221–31

  • Mazza R and Dimitrova V 2007 CourseVis: A graphical student monitoring tool for supporting instructors in web-based distance courses Int. J. Hum. Comput. Stud. 65 125–39

  • Macfadyen L P and Dawson S 2010 Mining LMS data to develop an “early warning system” for educators: A proof of concept Comput. Educ. 54 588–99

  • Richard Lynch M D 2002 The Relationship Between Self-Regulation and Online Learning in a Blended Learning Context Int. Rev. Res. an open Distrib. Learn.

  • You J W 2016 Identifying significant indicators using LMS data to predict course achievement in online learning Internet High. Educ. 29 23–30

  • Pardo A Data Capturing Mechanisms for Learning Analytics

  • Baker R S J D, Yacef K, et al 2012 Learning analytics for online discussions: a pedagogical model for intervention with embedded and extracted analytics Am. Behav. Sci. 15 2–5

  • Lane D Logic of Hypothesis Testing - David Lane

  • Linda Baer J C Chapter 4: From Metrics to Analytics, Reporting to Action: Analyticsâ€TM Role in Changing the Learning Environment Educ. Publ.

  • Anon Student Intervention Guide Learn. Anal. Educ. data Min. Learn. impact

  • Amrieh E A, Hamtini T and Aljarah I 2015 Preprocessing and analyzing educational data set using X-API for improving student’s performance 2015 IEEE Jordan Conf. Appl. Electr. Eng. Comput. Technol. 1–5

  • Chen J F, Hsieh H N and Do Q H 2014 Predicting student academic performance: A comparison of two meta-heuristic algorithms inspired by cuckoo birds for training neural networks Algorithms 7 538–53.


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  • A Learning Analytics Approach for Student Performance Assessment

Abstract Views: 360  |  PDF Views: 157

Authors

Mohamed H. Haggag
Department of Computer Science, Helwan University, Cairo, Egypt
Mahmood Abdel Latif
Department of Computer Science, Helwan University, Cairo, Egypt
Deena Mostafa Helal
Department of Computer Science, Helwan University, Cairo, Egypt

Abstract


Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement.

Keywords


Big Data, Student Success, Performance Indicators.

References