Visualizing Data to Support Judgement, Inference, and Decision Making in Learning Analytics: Insights from Cognitive Psychology and Visualization Science

被引:39
作者
Alhadad, Sakinah S. J. [1 ]
机构
[1] Griffith Univ, Ctr Learning Futures, Nathan Campus, Brisbane, Qld 4122, Australia
来源
JOURNAL OF LEARNING ANALYTICS | 2018年 / 5卷 / 02期
关键词
Data visualisation; attention; cognition; learning analytics; research methodology;
D O I
10.18608/jla.2018.52.5
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Understanding human judgement and decision making during visual inspection of data is of both practical and theoretical interest. While visualizing data is a commonly employed mechanism to support complex cognitive processes such as inference, judgement, and decision making, the process of supporting and scaffolding cognition through effective design is less well understood. Applying insights from cognitive psychology and visualization science, this paper critically discusses the role of human factors - visual attention, perception, judgement, and decision making - toward informing methodological choices when visualizing data. The value of visualizing data is discussed in two key domains: 1) visualizing data as a means of communication; and 2) visualizing data as research methodology. The first applies cognitive science principles and research evidence to inform data visualization design for communication. The second applies data- and cognitive-science to deepen our understanding of data, of its uncertainty, and of analysis when making inferences. The evidence for human capacity limitations - attention and cognition - are discussed in the context of data visualizations to support inference-making in both domains, and are followed by recommendations. Finally, how learning analytics can further research on understanding the role data visualizations can play in supporting complex cognition is proposed.
引用
收藏
页码:60 / 85
页数:26
相关论文
共 142 条
[71]   A Critical Review of Line Graphs in Behavior Analytic Journals [J].
Kubina, Richard M., Jr. ;
Kostewicz, Douglas E. ;
Brennan, Kaitlyn M. ;
King, Seth A. .
EDUCATIONAL PSYCHOLOGY REVIEW, 2017, 29 (03) :583-598
[72]   Interpreting histograms. As easy as it seems? [J].
Lem, Stephanie ;
Onghena, Patrick ;
Verschaffel, Lieven ;
Van Dooren, Wim .
EUROPEAN JOURNAL OF PSYCHOLOGY OF EDUCATION, 2014, 29 (04) :557-575
[73]   On the misinterpretation of histograms and box plots [J].
Lem, Stephanie ;
Onghena, Patrick ;
Verschaffel, Lieven ;
Van Dooren, Wim .
EDUCATIONAL PSYCHOLOGY, 2013, 33 (02) :155-174
[74]   Inferring Learning from Big Data: The Importance of a Transdisciplinary and Multidimensional Approach [J].
Lodge J.M. ;
Alhadad S.S.J. ;
Lewis M.J. ;
Gašević D. .
Technology, Knowledge and Learning, 2017, 22 (03) :385-400
[75]   Measurement error and the replication crisis [J].
Loken, Eric ;
Gelman, Andrew .
SCIENCE, 2017, 355 (6325) :584-585
[76]   Effective communication of standard errors and confidence intervals [J].
Louis, Thomas A. ;
Zeger, Scott L. .
BIOSTATISTICS, 2009, 10 (01) :1-2
[77]  
Lund K., 2017, J LEARNING ANAL, V4, P102, DOI [DOI 10.18608/JLA.2017.43.6, 10.18608/jla.2017.43.6]
[78]  
Matejka J., 2017, DATASAURUS DOZEN SAM
[79]   Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing [J].
Matejka, Justin ;
Fitzmaurice, George .
PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), 2017, :1290-1294
[80]   The Effect of Visual Appearance on the Performance of Continuous Sliders and Visual Analogue Scales [J].
Matejka, Justin ;
Glueck, Michael ;
Grossman, Tovi ;
Fitzmaurice, George .
34TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2016, 2016, :5421-5432