Does E-Learning Content Design Affect Student Learning Outcomes?

Nisail Mugni Hidayati, , Indonesia


This study aims to determine and analyze the effect of e-learning content design in the economic subject on learning outcomes moderated by student gender. The level of content design is measured through indicators of interface design, functionality, system support, usability, convenience, satisfaction, goals, active learning style, basic concepts, problem solving and experience. The level of learning outcomes is measured through the indicator of the test score results. This study uses an explanatory survey method with data collection techniques through questionnaires those are distributed to 197 students of the Social Sciences major with a sample of 132 students at SMA Negeri 1 Rengasdengklok. The data analysis technique uses descriptive statistics, inferential statistics and regression analysis. The results of this study indicate 1) The level of content design has a positive effect on the level of learning outcomes. The higher the level of e-learning content design on the economic subject, the higher the learning outcomes will be obtained by students. 2) Gender does not moderate the positive effect of the level of content design on the level of learning outcomes. However, the positive influence on content design for the male group is stronger.


Content Design; Gender; Learning Outcomes

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Abbiss, J. (2008). Rethinking The “Problem” Of Gender And IT Schooling: Discourses In Literature. Gender And Education, 20(2), 153–165. Https://Doi.Org/10.1080/09540250701805839

Adrián Moneta Pizarro, Mariana González, Carina Tofful, Mercedes Arrieta, V. B. (2020). A Proposal For A Structural Equation Model To Explain Academic Performance In E-Learning. Research In Production And Development, 6(422), 1–11.

Alameri, J., Masadeh, R., Hamadallah, E., Ismail, H. B., & Fakhouri, H. N. (2020). Students ’ Perceptions Of E-Learning Platforms ( Moodle , Microsoft Teams And Zoom Platforms ) In The University Of Jordan Education And Its Relation To Self-Study And Academic Achievement During COVID-19 Pandemic. Advanced Research & Studies Journal |, 11(5), 21–33.

Alwadei, A. H., Tekian, A. S., Brown, B. P., Alwadei, F. H., Park, Y. S., Alwadei, S. H., & Harris, I. B. (2020). Effectiveness Of An Adaptive Elearning Intervention On Dental Students’ Learning In Comparison To Traditional Instruction. Journal Of Dental Education, June, 1–9. Https://Doi.Org/10.1002/Jdd.12312

Bean, J. P., & Metzner, B. (1987). The Estimation Of A Conceptual Model Of Nontraditional Undergraduate Student Attrition. Research In Higher Education, 27(1), 15–38.

Chen, K. C., & Jang, S. J. (2010). Motivation In Online Learning: Testing A Model Of Self-Determination Theory. Computers In Human Behavior, 26(4), 741–752. Https://Doi.Org/10.1016/J.Chb.2010.01.011

Cho, V., Cheng, T. C. E., & Lai, W. M. J. (2009). The Role Of Perceived User-Interface Design In Continued Usage Intention Of Self-Paced E-Learning Tools. Computers And Education, 53(2), 216–227. Https://Doi.Org/10.1016/J.Compedu.2009.01.014

Chu, R. J. Chun. (2010). How Family Support And Internet Self-Efficacy Influence The Effects Of E-Learning Among Higher Aged Adults - Analyses Of Gender And Age Differences. Computers And Education, 55(1), 255–264. Https://Doi.Org/10.1016/J.Compedu.2010.01.011

Chung, L. Y., & Chang, R. C. (2017). The Effect Of Gender On Motivation And Student Achievement In Digital Game-Based Learning: A Case Study Of A Contented-Based Classroom. Eurasia Journal Of Mathematics, Science And Technology Education, 13(6), 2309–2327. Https://Doi.Org/10.12973/EURASIA.2017.01227A

Comeaux, E., & Harrison, K. C. (2011). A Conceptual Model Of Academic Success For Student-Athletes. Educational Researcher, 40(5), 235–245. Https://Doi.Org/10.3102/0013189X11415260

Cuadrado-García, M., Ruiz-Molina, M. E., & Montoro-Pons, J. D. (2010). Are There Gender Differences In E-Learning Use And Assessment? Evidence From An Interuniversity Online Project In Europe. Procedia - Social And Behavioral Sciences, 2(2), 367–371. Https://Doi.Org/10.1016/J.Sbspro.2010.03.027

Ćukušić, M., Alfirević, N., Granić, A., & Garača, Ž. (2010). E-Learning Process Management And The E-Learning Performance: Results Of A European Empirical Study. Computers And Education, 55(2), 554–565. Https://Doi.Org/10.1016/J.Compedu.2010.02.017

Dalvi-Esfahani, M., Wai Leong, L., Ibrahim, O., & Nilashi, M. (2020). Explaining Students’ Continuance Intention To Use Mobile Web 2.0 Learning And Their Perceived Learning: An Integrated Approach. Journal Of Educational Computing Research, 57(8), 1956–2005. Https://Doi.Org/10.1177/0735633118805211

Durndell, A., & Haag, Z. (2002). Computer Self Efficacy, Computer Anxiety, Attitudes Towards The Internet And Reported Experience With The Internet, By Gender, In An East European Sample. Computers In Human Behavior, 18(5), 521–535. Https://Doi.Org/10.1016/S0747-5632(02)00006-7

Fenollar, P., Román, S., & Cuestas, P. J. (2007). University Students’ Academic Performance: An Integrative Conceptual Framework And Empirical Analysis. British Journal Of Educational Psychology, 77(4), 873–891. Https://Doi.Org/10.1348/000709907X189118

Holsapple, C. W., & Lee-Post, A. (2006). Defining, Assessing, And Promoting E-Learning Success: An Information Systems Perspective*. Decision Sciences Journal Of Innovative Education, 4(1), 67–85. Https://Doi.Org/10.1111/J.1540-4609.2006.00102.X

James Dalziel. (2003). Implementing Learning Design: The Learning Activity Management System (Lams). Proceedings Of The 20th Annual Conference Of The Australasian Society For Computers In Learning In Tertiary Education (ASCILITE).

Joseph Owan, V., Asuquo Bassey, B., Omorobi Omorobi, G., & Uwase Esuong, U. (2020). Poll Everywhere E-Learning Platform, Test Anxiety, And Undergraduates’ Academic Performance In Mathematics: Empirical Evidence From Nigeria. American Journal Of Social Sciences And Humanities, 5(1), 141–150. Https://Doi.Org/10.20448/801.51.141.150

Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® And Academic Performance. Computers In Human Behavior, 26(6), 1237–1245. Https://Doi.Org/10.1016/J.Chb.2010.03.024

Koper, R., & Olivier, B. (2004). Representing The Learning Design Of Units Of Learning. Journal Of Educational Technology & Society, 7(3), 97–111.

Ozkan, S., & Koseler, R. (2009). Multi-Dimensional Students’ Evaluation Of E-Learning Systems In The Higher Education Context: An Empirical Investigation. Computers And Education, 53(4), 1285–1296. Https://Doi.Org/10.1016/J.Compedu.2009.06.011

Pituch, K. A., & Lee, Y. Kuei. (2006). The Influence Of System Characteristics On E-Learning Use. Computers And Education, 47(2), 222–244. Https://Doi.Org/10.1016/J.Compedu.2004.10.007

Reeder, C. W. (1942). Academic Performance. The Journal Of Higher Education, 13(4), 204. Https://Doi.Org/10.2307/1975819

Riduwan, & Kuncoro. (2012). Cara Menggunakandan Memakai Path Analysis (Analisis Jalur). Bandung: Alfabeta.

Rodríguez-Ardura, I., & Meseguer-Artola, A. (2019). Flow Experiences In Personalised E-Learning Environments And The Role Of Gender And Academic Performance. Interactive Learning Environments, 0(0), 1–24. Https://Doi.Org/10.1080/10494820.2019.1572628

Shee, D. Y., & Wang, Y. S. (2008). Multi-Criteria Evaluation Of The Web-Based E-Learning System: A Methodology Based On Learner Satisfaction And Its Applications. Computers And Education, 50(3), 894–905. Https://Doi.Org/10.1016/J.Compedu.2006.09.005

Stajkovic, A. D., Bandura, A., Locke, E. A., Lee, D., & Sergent, K. (2018). Test Of Three Conceptual Models Of Influence Of The Big Five Personality Traits And Self-Efficacy On Academic Performance: A Meta-Analytic Path-Analysis. Personality And Individual Differences, 120(August 2017), 238–245. Https://Doi.Org/10.1016/J.Paid.2017.08.014

Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What Drives A Successful E-Learning? An Empirical Investigation Of The Critical Factors Influencing Learner Satisfaction. Computers And Education, 50(4), 1183–1202. Https://Doi.Org/10.1016/J.Compedu.2006.11.007

T. Yuniarsih *, K. Kusnendi, L. A. W. (2018). The Influence Of Knowledge Sharing On Innovation. Economics, Business And Management Research, 117, 193–198. Https://Doi.Org/10.1108/09555341011040994

Ullrich, C., Borau, K., Luo, H., Tan, X., Shen, L., & Shen, R. (2008). Why Web 2.0 Is Good For Learning And For Research. 705. Https://Doi.Org/10.1145/1367497.1367593

Urban, K., & Urban, M. (2020). Effects Of Performance Feedback And Repeated Experience On Self-Evaluation Accuracy In High- And Low-Performing Preschool Children. European Journal Of Psychology Of Education. Https://Doi.Org/10.1007/S10212-019-00460-6

Welsh, E. T., Wanberg, C. R., Brown, K. G., & Simmering, M. J. (2003). E-Learning: Emerging Uses, Empirical Results And Future Directions. International Journal Of Training And Development, 7(4), 245–258. Https://Doi.Org/10.1046/J.1360-3736.2003.00184.X

Windholz, G. (1983). Pavlov’s Position Toward American Behaviorism. Journal Of The History Of The Behavioral Sciences, 19(4), 394–407. Https://Doi.Org/10.1002/1520-6696(198310)19:4<394::AID-JHBS2300190408>3.0.CO;2-F

Wongwatkit, C., Panjaburee, P., Srisawasdi, N., & Seprum, P. (2020). Moderating Effects Of Gender Differences On The Relationships Between Perceived Learning Support, Intention To Use, And Learning Performance In A Personalized E-Learning. Journal of Computers in Education, 0123456789.



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