|
|
Progress of Foreign Cognitive Load Theory Application Research |
Zha Xianjin1,2, Huang Chengsong1,4, Yan Yalan3, Guo Jia1 |
1.School of Information Management, Wuhan University, Wuhan 430072 2.Center for Studies of Information Resources, Wuhan University, Wuhan 430072 3.Evergrande School of Management, Wuhan University of Science and Technology, Wuhan 430065 4.Laboratory Center for Library and Information Science, Wuhan University, Wuhan 430072 |
|
|
Abstract Cognition refers to the process and capacity of an individual s knowledge acquisition and problem-solving, which is also the capability of an individual to process information. The cognitive load theory suggests that an individual s cognition is a type of resource consumption. Individuals need to process cognition and thus consume cognitive resources during the process of knowledge acquisition and problem solving. The main impacting factors of cognitive load include individuals prior experience, intrinsic nature of learning materials, and the approach toward organizing and presenting learning materials. The cognitive load theory has been extensively applied in the fields of Education, Psychology, Computer Science, Business Economics, and Information Science Library Science. The work that proposed the cognitive load theory was treated as the seed paper in our current study. Using the Social Sciences Citation Index/Science Citation Index, a word frequency statistics analysis was conducted based on the keywords provided by the studies citing the seed paper. This study further presented the hot topics of cognitive load theory application research from the citing paper perspective. The method of literature study was then employed, and four subject categories were elicited based on the contents of the citing papers, such as impacting factors and manifestation of cognitive load, impact of cognitive load on learning, impact of cognitive load on behaviors of information system users, and impact of cognitive load on collaborative behaviors. The development trend of cognitive load theory application research was reviewed in terms of these four subject categories.
|
Received: 14 May 2019
|
|
|
|
1 SwellerJ. Cognitive load during problem solving: Effects on learning[J]. Cognitive Science, 1988, 12(2): 257-285. 2 SimonH A. Information-processing models of cognition[J]. Journal of the American Society for Information Science, 1981, 32(5): 364-377. 3 SwellerJ. Cognitive load theory, learning difficulty, and instructional design[J]. Learning and Instruction, 1994, 4(4): 295-312. 4 SwellerJ, PaasF. Cognitive bases of human creativity[J]. Educational Psychology Review, 2009, 21(1): 11-19. 5 SwellerJ. Element interactivity and intrinsic, extraneous, and germane cognitive load[J]. Educational Psychology Review, 2010, 22(2): 123-138. 6 Tague-SutcliffeJ. An introduction to informetrics[J]. Information Processing & Management, 1992, 28(1): 1-3. 7 刘启元, 叶鹰. 文献题录信息挖掘技术方法及其软件SATI的实现——以中外图书情报学为例[J]. 信息资源管理学报, 2012, 2(1): 50-58. 8 OldroydJ B, MorrisS S. Catching falling stars: A human resource response to social capital’s detrimental effect of information overload on star employees[J]. Academy of Management Review, 2012, 37(3): 396-418. 9 EpplerM J, MengisJ. The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines[J]. The Information Society, 2004, 20(5): 325-344. 10 BawdenD, HolthamC, CourtneyN. Perspectives on information overload[J]. Aslib Proceedings, 1999, 51(8): 249-255. 11 KockN, ParenteR, VervilleJ. Can Hofstede s model explain national differences in perceived information overload? A look at data from the US and New Zealand[J]. IEEE Transactions on Professional Communication, 2008, 51(1): 33-49. 12 YeQ, LawR, GuB, et al. The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings[J]. Computers in Human Behavior, 2011, 27(2): 634-639. 13 DangY, ZhangY L, ChenH C, et al. Theory-informed design and evaluation of an advanced search and knowledge mapping system in nanotechnology[J]. Journal of Management Information Systems, 2012, 28(4): 99-128. 14 FiglK, MendlingJ, StrembeckM. The influence of notational deficiencies on process model comprehension[J]. Journal of the Association for Information Systems, 2013, 14(6): 312-338. 15 KhacharemA, ZoudjiB, SpanjersI A E, et al. Improving learning from animated soccer scenes: Evidence for the expertise reversal effect[J]. Computers in Human Behavior, 2014, 35: 339-349. 16 UstunelZ, GunduzT. Human-robot collaboration on an assembly work with extended cognition approach[J]. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2017, 11(5): JAMDSM0057. 17 SaundersC, WienerM, KlettS, et al. The impact of mental representations on ICT-related overload in the use of mobile phones[J]. Journal of Management Information Systems, 2017, 34(3): 803-825. 18 HuJ H, HuH F, FangX. Examining the mediating roles of cognitive load and performance outcomes in user satisfaction with a website: A field quasi-experiment[J]. MIS Quarterly, 2017, 41(3): 975-987. 19 HorskyJ, DruckerE A, RamelsonH Z. Higher accuracy of complex medication reconciliation through improved design of electronic tools[J]. Journal of the American Medical Informatics Association, 2018, 25(5): 465-475. 20 Cater-SteelA, HineM J, GrantG. Embedding IT service management in the academic curriculum: A cross-national comparison[J]. Journal of Global Information Technology Management, 2010, 13(4): 64-92. 21 KimN S, KhalifeD, JudgeK A, et al. Visual causal models enhance clinical explanations of treatments for generalized anxiety disorder[J]. Journal of Health Communication, 2013, 18(sup 1): 103-117. 22 RiasR M, ZamanH B, ManapA A. Applying redundancy and animation in a multimedia learning application on a computer science domain[C]// Proceedings of the Knowledge Management International Conference. Sintok: Universiti Utara Malaysia, 2014: 273-278. 23 MullinsK. Good IDEA: Instructional design model for integrating information literacy[J]. The Journal of Academic Librarianship, 2014, 40(3-4): 339-349. 24 MullinsK. IDEA model from theory to practice: Integrating information literacy in academic courses[J]. The Journal of Academic Librarianship, 2016, 42(1): 55-64. 25 SaparovaD, NolanN S. Evaluating the appropriateness of electronic information resources for learning[J]. Journal of the Medical Library Association, 2016, 104(1): 24-32. 26 GrayP H, DurcikovaA. The role of knowledge repositories in technical support environments: Speed versus learning in user performance[J]. Journal of Management Information Systems, 2005, 22(3): 159-190. 27 WangQ Z, YangS, LiuM L, et al. An eye-tracking study of website complexity from cognitive load perspective[J]. Decision Support Systems, 2014, 62: 1-10. 28 XuJ D, BenbasatI, CenfetelliR T. The nature and consequences of trade-off transparency in the context of recommendation agents[J]. MIS Quarterly, 2014, 38(2): 379-406. 29 LiangH G, PengZ Y, XueY J, et al. Employees exploration of complex systems: An integrative view[J]. Journal of Management Information Systems, 2015, 32(1): 322-357. 30 WilsonT D. Models in information behaviour research[J]. Journal of Documentation, 1999, 55(3): 249-270. 31 LiuC C, LiuK P, WangP H, et al. Applying tangible story avatars to enhance children's collaborative storytelling[J]. British Journal of Educational Technology, 2012, 43(1): 39-51. 32 ShahC. Collaborative information seeking[J]. Journal of the Association for Information Science and Technology, 2014, 65(2): 215-236. 33 NaK, LeeJ. When two heads are better than one: Query behavior, cognitive load, search time, and task type in pairs versus individuals[J]. Aslib Journal of Information Management, 2016, 68(5): 545-565. 34 ReychavI, WuD Z. The interplay between cognitive task complexity and user interaction in mobile collaborative training[J]. Computers in Human Behavior, 2016, 62: 333-345. |
|
|
|