Hypermedia Learning Environment Development to Enhance Self-Regulated Learning Based on Self-Monitoring Skills
The use of learning media is currently growing rapidly. Today, many studies use computers as adaptive learning media for students; one example is the hypermedia learning environment (HLE). HLE media was developed to assist students in learning, such as the current situation of the Corona Virus Disease 2019 (COVID-19) pandemic which requires all learning activities to be carried out online. One of those affected fields is the education field, where all learning activities are transferred online, so HLE web-based learning can help students to keep learning from home. HLE is currently being developed to improve students’ abilities in the self-regulated learning (SRL) process. In SRL, there is an important component in it, namely self-monitoring. However, in its development, the developed HLE is not based on self-monitoring. In this study, an adaptive HLE was developed based on students’ self-monitoring abilities. In its development, the HLE system used the agile development method, namely Scrum. The initial data collection for student classification was the self-regulatory inventory (SRI). SRI was used as an instrument to measure students’ self-monitoring ability. The data were then processed to classify students into three classes, namely high, medium, and low. Subsequently, the results of the classification of student abilities were used to develop learning aids in HLE. The development assistance provided was in the form of text and videos that were adjusted to the level of student self-monitoring. From the results of the development, it was found that all HLE functions could run well. The system was tested on twelve students to determine the level of usability by using the system usability scale (SUS). The results were classified as good category, with a score of 72.92. Further research can apply this method to students and measure the effectiveness of the system that has been developed.
V. Aleven, “A is for Adaptivity, but What is Adaptivity? Re-Defining the Field of AIED Intuitively,” Proc. Workshop 17th Int. Conf. Artif. Intell. Educ. AIED 2015, 2015, hal. 11–20.
G. Schraw and D. Moshman, “Metacognitive Theories,” Educ. Psychol. Rev., Vol. 7, No. 4, pp. 351–371, 1995.
L. Bacon and L. Mackinnon, “A Flexible Framework for Metacognitive Modeling and Development,” Proc. Int. Conf. e-Learn. (ICEL), 2014, pp. 7–14.
E.G. Poitras and S.P. Lajoie, “A Domain-Specific Account of Self-Regulated Learning: The Cognitive and Metacognitive Activities Involved in Learning Through Historical Inquiry,” Metacogn. Learn., Vol. 8, No. 12, pp. 213–234, 2013.
S. Sanchez-Alonso and Y. Vovides, “Integration of Metacognitive Skills in the Design of Learning Objects,” Comput. Human Behav., Vol. 23, No. 6, pp. 2585–2595, 2007.
J. Kalenda, “Self-Regulated Learning in Students of Helping Professions,” Procedia Soc. Behav. Sci., Vol. 217, pp. 282–292, 2016.
B.J. Zimmerman and A.S. Paulsen, “Self‐Monitoring During Collegiate Studying: An Invaluable Tool for Academic Self‐Regulation,” New Direction Teach. Learn., Vol. 1995, No. 63, pp. 13–27, 1995.
M. Martinez-Pons and B.J. Zimmerman, “Construct Validation of a Strategy Model of Student Self-Regulated Learning,” J. Educ. Psychol., Vol. 80, No. 3, pp. 284–290, 1988.
M. Snyder, “Self-Monitoring of Expressive Behavior,” J. Pers., Soc. Psychol., Vol. 30, No. 4, pp. 526–537, 1974.
P.R. Pintrich, C.A. Wolters, and G.P. Baxter, “Assessing Metacognition and Self-Regulated Learning,” in Issues in the Measurement of Metacognition, G. Schraw and J.C. Impara, Eds., Nebraska, USA: Buros Institute of Mental Measurement, 2000, pp. 43–97, 2000.
E.S. Shapiro and C.L. Cole, “Self-Monitoring in Assessing Children’s Problems,” Vol. 11, No. 4, pp. 448–457, 1999.
D.H. Schunk, “Goal Setting and Self-Efficacy During Self-Regulated Learning,” Educ. Psychol., Vol. 25, No. 1, pp. 71–86, 1990.
W.Y. Lan, “The Effects of Self-Monitoring on Students’ Course Performance, Use of Learning Strategies, Attitude, Self-Judgment Ability,” J. Exp. Educ., Vol. 64, No. 2, pp. 101–115, 1996.
H.P. Wills and B.A. Mason, “Implementation of a Self-Monitoring Application to Improve On-Task Behavior: A High School Pilot Study,” J. Behav. Educ., Vol. 23, No. 4, pp. 421–434, 2014.
S. Arslantas and A. Kurnaz, “The Effect of Using Self-Monitoring Strategies in Social Studies Course on Self-Monitoring, Self-Regulation and Academic Achievement,” Int. J. Res. Educ. Sci., Vol. 3, No. 2, pp. 452–463, 2017.
A. Benmimoun and P. Trigano, “Self Regulated Learning Provided by Hypermedia and the Use of Technology Enhanced Learning Environments,” Int. Conf. Web Intell., Intell. Agent Technol., 2009, pp. 211–214.
J.M. Su, “A Self-Regulated Learning System to Support Adaptive Scaffolding in Hypermedia-Based Learning Environments,” Proc. - 2014 7th Int. Conf. Ubi-Media Comput. Work. U-MEDIA 2014, 2014, pp. 326–331.
A. Nurlayli, “Adaptive HLE untuk Mendukung Self-Regulated Learning (Studi Kasus: Matakuliah Algoritme dan Struktur Data),” Master’s thesis, Universitas Gadjah Mada, Yogyakarta, Indonesia, 2018.
A. Nurlayli, T. B. Adji, A. E. Permanasari, and I. Hidayah, “Tahani Model of Fuzzy Database for an Adaptive Metacognitive Scaffolding in Hypermedia Learning Environment (Case: Algorithm and Structure Data Course),” 2017 Int. Conf. Sustain. Inf. Eng., Technol. (SIET), 2017, pp. 358–363.
I.S. Sakkinah, R. Hartanto, and A.E. Permanasari, “Students’ Self-Monitoring Skill Classification in Learning Activities,” Adv. Soc. Sci. Educ. Humanit. Res., Vol. 440, pp. 66–69, 2020.
J. Brooke, “SUS - A Quick and Dirty Usability Scale,” in Usability Evaluation in Industry, P.W. Jordan, B. Thomas, I.L. McClelland, B. Weerdmeester, Eds., London, England: CRC Press, 1996, ch. 21, pp. 189–194.
A. Bangor, et al., “Determining What Individual SUS Scores Mean: Adding an Adjective Rating Scale,” J. Usability Scales, Vol. 4, No. 3, pp. 114–123, 2009.