With the widespread use of the Internet, adopting e-learning systems in courses has gradually become more and more important in universities in Taiwan. However, because of limitations of teachers' time, selecting suitable online IT tools has become very important. This study proposes an analytic hierarchy process (AHP)–multi-choice goal programming (MCGP) evaluation model as a decision aid to help decision makers obtain appropriate online IT tools considering the time limitations of teachers and students. To demonstrate the usefulness of the proposed method, this study implements the evaluation model in computer courses at a university in central Taiwan. For different purposes, teachers can set different time limitations and aspiration levels for each goal to rank the range of possible IT tools. With the AHP–MCGP evaluation model, teachers can find the most suitable time for the allocation of possible IT tools in the e-learning system to improve students' learning practice.
Get full access to this article
View all access options for this article.
References
1.
AdeyinkaT.MutulaS. (2010). A proposed model for evaluating the success of WebCT course content management system. Computers in Human Behavior, 26, 1795–1805.
2.
AlptekinS. E.KarsakE. E. (2011). An integrated decision framework for evaluating and selecting e-learning products. Applied Soft Computing, 11, 2990–2998.
3.
ChangC.-T. (2007). Multi-choice goal programming. Omega: Journal of Death and Dying, 35, 389–396.
ChangC.-T.HoH.-P.KuC.-Y.LiaoC.-C. (2011). A MCGP decision aid for homebuyer to make the best choice. Quality and Quantity, 45, 969–983.
6.
ChangC.-T.KuC.-Y.HoH.-P. (2010). Fuzzy multi-choice goal programming for supplier selection. International Journal of Operations Research and Information Systems, 1(3), 28–52.
7.
ChaoR.-J.ChenY.-H. (2009). Evaluation of the criteria and effectiveness of distance e-learning with consistent fuzzy preference relations. Expert Systems with Applications, 36, 10657–10662.
8.
ChenY.-T.ZhengC.ChangC.-T. (2011). 3-level MCGP: An efficient algorithm for MCGP in solving multi-forest management problems. Scandinavian Journal of Forest Research, 26, 457–465.
9.
CostaD. S. J.MullanB. A.KotheE. J.ButowP. (2010). A web-based formative assessment tool for Masters students: A pilot study. Computers & Education, 54(4), 1248–1253.
10.
HashemiM.AzizinezhadM.FarokhiM. (2012). PowerPoint as an innovative tool for teaching and learning in modern classes. Procedia – Social and Behavioral Sciences, 31, 559–563.
11.
HewK. F.CheungW. S. (2003). Evaluating the participation and quality of thinking of pre-service teachers in an asynchronous online discussion environment: Part I. International Journal of Instructional Media, 30, 247–262.
12.
Instructional Technology Council (ITC). (1998). Instructional Technology Council's definition of distance education. Available at http://www.itcnetwork.org/definition.htm
13.
LeeA.KangH.-Y.YangC.-Y.LinC.-Y. (2010). An evaluation framework for product planning using FANP, QFD and multi-choice goal programming. International Journal of Production Research, 48(13), 3977–3997.
14.
LeeC. H. B. (2011). Effect of incentivized online activities on e-learning. Procedia – Social and Behavioral Sciences, 28, 211–216.
15.
LeeH.KimJ. W.HackneyR. (2011). Knowledge hoarding and user acceptance of online discussion board systems in eLearning: A case study. Computers in Human Behavior, 27(4), 1431–1437.
16.
LiawS.-S. (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51, 864–873.
17.
PaechterM.MaierB.MacherD. (2010). Students' expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction. Computers & Education, 54, 222–229.
18.
SaatyT. L. (1980). The analytical hierarchy process: Planning, priority setting. In Resource Allocation. New York, NY: McGraw-Hill.
19.
SchrageL. (2002). LINGO Release 8.0, LINDO System, Inc.
20.
SealeJ.CooperM. (2010). E-learning and accessibility: An exploration of the potential role of generic pedagogical tools. Computers & Education, 54, 1107–1116.
21.
SheeD. Y.WangY.-S. (2008). “Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers & Education, 50, 894–905.
22.
SladekP.MilerT.BenarovaR. (2011). How to increase students' interest in science and technology. Procedia – Social and Behavioral Sciences, 12, 168–174.
23.
SunP.-C.TsaiR. J.FingerG.ChenY.-Y.YehD. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50, 1183–1202.
24.
TangT. L.-P.AustinM. J. (2009). Students' perceptions of teaching technologies, application of technologies, and academic performance. Computers & Education, 53(4), 1241–1255.
25.
ThurmondV. A.WambachK.ConnorsH. R.FreyB. B. (2002). Evaluation of student satisfaction: Determining the impact of a web-based environment by controlling for student characteristics. The American Journal of Distance Education, 16(3), 169–189.
26.
WangT.-H. (2008). Web-based quiz-game-like formative assessment: Development and evaluation. Computers & Education, 51, 1247–1263.
27.
WangT.-H. (2011). Developing web-based assessment strategies for facilitating junior high school students to perform self-regulated learning in an e-learning environment. Computers & Education, 57(2), 1801–1812.
28.
ZhangD.ZhouL.BriggsR. O.NunamakerJ. F. (2006). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & Management, 43, 15–27.
