Table 1.
Search Topics and Their Specificity
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doi: 10.3934/bdia.2017019
User perceived learning from interactive searching on big medical literature data
School of Information Sciences, Wayne State University, 106 Kresge Library, Detroit, MI 48202, USA |
As in other fields, search engines have been heavily used as an information accessing tool for massive amount of medical literature data. This research investigates the user's learning during interactive searching process with the PubMed data, to find out what search behaviors would be associated with the user's perceived learning, and whether or not the user's perceived learning could be reflected in the existing search performance measures, so that such measures could also be used for indicating learning during searching process. The research used a data set collected by a research project on searching, which involved 35 participants at a major US university. The results show that the number of documents saved is significantly correlated with perceived learning for all search topics. None of the classical search performance measures is correlated with perceived learning. However, for specific topics, one of the performance measures, Recall, is significantly correlated with perceived learning. The results and the implications of the findings are discussed.
Keywords:
Information interactions,
learning by searching,
searching as learning,
perceived learning,
searching behavior.
Mathematics Subject Classification:
Primary: 97R50; Secondary: 97R71.
Citation:
Xiangmin Zhang.
User perceived learning from interactive searching on big medical literature data. Big Data & Information Analytics, doi: 10.3934/bdia.2017019
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References:
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S. A. Ambrose, M. W. Bridges, M. DiPietro, M. C. Lovett and M. K. Norman, How Learning Works: Seven Research-Based Principles for Smart Teaching, Jossey-Bass, A Wiley Imprint. P. 3, 2010. |
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L. W. Anderson and D. A. Krathwohl, A Taxonomy for Learning, Teaching, and assessing: A Revision of Bloom's Taxonomy of Educational Objectives, New York: Longman, 2001. |
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N. J. Belkin, R. N. Oddy and H. M. Brooks,
ASK for information retrieval: Part 1: Background and theory, Journal of Documentation, 38 (1982), 61-71.
doi: 10.1108/eb026722. |
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Bransford, J. D., Brown, A. L. and R. R. Cocking, How People Learn: Brain, Mind, Experience, and School, Washington: National Academies Press, 2000. |
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C. Bruce and H. Hughes,
Informed learning: A pedagogical construct attending simultaneously to information, use and learning, Library & Information Science Research, 32 (2010), A2-A8.
doi: 10.1016/j.lisr.2010.07.013. |
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Downes, Learning Objects: Resources For Distance Education Worldwide, International Review of Research in Open and Distance Learning, 2001. |
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G. B. Duggan and S. J. Payne, Knowledge in the head and on the web: Using topic expertise to aid search,
CHI '08 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2008, 39-48.
doi: 10.1145/1357054.1357062. |
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D. C. Edelson, D. N. Gordin and R. D. Pea, Addressing the challenges of inquiry-based learning through technology and curriculum design, Journal of the Learning Sciences, 8 (1999), 391-450. |
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R. Farwick, O. J. L. Hester and W. H. Teale, Where do you want to go today? Inquiry-based learning and technology integration, The Reading Teacher, 55 (2002), 616-625. |
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S. R. Goldman, J. Braasch, J. Wiley, A. Graesser and K. Brodowinska, Comprehending and learning from internet sources: Processing patterns of better and poorer learners, Reading Research Quarterly, 47 (2012), 356-381. |
| [11] |
W. Hersh and E. Voorhees,
TREC genomics special issue overview, Information Retrieval, 12 (2009), 1-15.
doi: 10.1007/s10791-008-9076-6. |
| [12] |
B. J. Jansen, D. Booth and B. Smith,
Using the taxonomy of cognitive learning to model online searching, Information Processing and Management, 45 (2009), 643-663.
doi: 10.1016/j.ipm.2009.05.004. |
| [13] |
C. Kuhlthau, Seeking Meaning, 2nd ed., Libraries Unlimited, Westport, CT, 2004. |
| [14] |
S. K. MacGregor and Y. Lou,
Web-based learning: How task scaffolding and web site design support knowledge acquisition, Journal of Research on Technology in Education, 37 (2004), 161-175.
doi: 10.1080/15391523.2004.10782431. |
| [15] |
G. Marchionini, Exploratory search: From finding to understanding, Communications of the ACM, 49 (2006), 41-46. |
| [16] |
G. Marchionini and H. Maurer,
The roles of digital libraries in teaching and learning, Commnication of the ACM, 38 (1995), 67-75.
doi: 10.1145/205323.205345. |
| [17] |
J. Ormrod, Educational Psychology? Developing Learners, 7th Ed., Pearson, New York, 2011. |
| [18] |
S. Y. Rieh, K. Collins-Thompson, P. Hansen and H. J. Lee,
Towards searching as a learning process: A review of current perspectives and future directions, Journal of Information Science, 42 (2016), 19-34.
doi: 10.1177/0165551515615841. |
| [19] |
J.-L. Shih, C.-W. Chuang and G.-J. Hwang, An Inquiry-based Mobile Learning Approach to Enhancing Social Science Learning Effectiveness, Educational Technology & Society, 13 (2010), 50-62. |
| [20] |
P. Vakkari,
Searching as learning: A systematization based on literature, Journal of Information Science, 42 (2016), 7-18.
doi: 10.1177/0165551515615833. |
| [21] |
C. J. Van Rijsbergen, Information Retrieval (2nd ed.), Butterworth, 1979. |
| [22] |
A. Walraven, S. Brand-Gruwel and H. P. A. Boshuizen, How students evaluate information and sources when searching the World Wide, Web for information. Computers & Education, 52 (2009), 234-246. |
| [23] |
T. Willoughby, S. A. Anderson, E. Woodc, J. Mueller and C. Ross,
Fast searching for information on the Internet to use in a learning context: The impact of domain knowledge, Computers & Education, 52 (2009), 640-648.
doi: 10.1016/j.compedu.2008.11.009. |
| [24] |
C. Yin, H.-Y. Sung, G.-J. Hwang, S. Hirokawa, H.-C. Chu, B. Flanagan and Y. Tabata,
Learning by Searching: A Learning Environment that Provides Searching and Analysis Facilities for Supporting Trend Analysis Activities, Educational Technology & Society, 14 (2013), 1865-1889.
|
| [25] |
X. Zhang, J. Liu, C. Liu and M. Cole, Factors influencing users? perceived learning during online searching, in: Proceedings of the 9th International Conference on e-Learning (ICEL-2014), 2014, 200-210. |
show all references
References:
| [1] |
S. A. Ambrose, M. W. Bridges, M. DiPietro, M. C. Lovett and M. K. Norman, How Learning Works: Seven Research-Based Principles for Smart Teaching, Jossey-Bass, A Wiley Imprint. P. 3, 2010. |
| [2] |
L. W. Anderson and D. A. Krathwohl, A Taxonomy for Learning, Teaching, and assessing: A Revision of Bloom's Taxonomy of Educational Objectives, New York: Longman, 2001. |
| [3] |
N. J. Belkin, R. N. Oddy and H. M. Brooks,
ASK for information retrieval: Part 1: Background and theory, Journal of Documentation, 38 (1982), 61-71.
doi: 10.1108/eb026722. |
| [4] |
Bransford, J. D., Brown, A. L. and R. R. Cocking, How People Learn: Brain, Mind, Experience, and School, Washington: National Academies Press, 2000. |
| [5] |
C. Bruce and H. Hughes,
Informed learning: A pedagogical construct attending simultaneously to information, use and learning, Library & Information Science Research, 32 (2010), A2-A8.
doi: 10.1016/j.lisr.2010.07.013. |
| [6] |
Downes, Learning Objects: Resources For Distance Education Worldwide, International Review of Research in Open and Distance Learning, 2001. |
| [7] |
G. B. Duggan and S. J. Payne, Knowledge in the head and on the web: Using topic expertise to aid search,
CHI '08 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2008, 39-48.
doi: 10.1145/1357054.1357062. |
| [8] |
D. C. Edelson, D. N. Gordin and R. D. Pea, Addressing the challenges of inquiry-based learning through technology and curriculum design, Journal of the Learning Sciences, 8 (1999), 391-450. |
| [9] |
R. Farwick, O. J. L. Hester and W. H. Teale, Where do you want to go today? Inquiry-based learning and technology integration, The Reading Teacher, 55 (2002), 616-625. |
| [10] |
S. R. Goldman, J. Braasch, J. Wiley, A. Graesser and K. Brodowinska, Comprehending and learning from internet sources: Processing patterns of better and poorer learners, Reading Research Quarterly, 47 (2012), 356-381. |
| [11] |
W. Hersh and E. Voorhees,
TREC genomics special issue overview, Information Retrieval, 12 (2009), 1-15.
doi: 10.1007/s10791-008-9076-6. |
| [12] |
B. J. Jansen, D. Booth and B. Smith,
Using the taxonomy of cognitive learning to model online searching, Information Processing and Management, 45 (2009), 643-663.
doi: 10.1016/j.ipm.2009.05.004. |
| [13] |
C. Kuhlthau, Seeking Meaning, 2nd ed., Libraries Unlimited, Westport, CT, 2004. |
| [14] |
S. K. MacGregor and Y. Lou,
Web-based learning: How task scaffolding and web site design support knowledge acquisition, Journal of Research on Technology in Education, 37 (2004), 161-175.
doi: 10.1080/15391523.2004.10782431. |
| [15] |
G. Marchionini, Exploratory search: From finding to understanding, Communications of the ACM, 49 (2006), 41-46. |
| [16] |
G. Marchionini and H. Maurer,
The roles of digital libraries in teaching and learning, Commnication of the ACM, 38 (1995), 67-75.
doi: 10.1145/205323.205345. |
| [17] |
J. Ormrod, Educational Psychology? Developing Learners, 7th Ed., Pearson, New York, 2011. |
| [18] |
S. Y. Rieh, K. Collins-Thompson, P. Hansen and H. J. Lee,
Towards searching as a learning process: A review of current perspectives and future directions, Journal of Information Science, 42 (2016), 19-34.
doi: 10.1177/0165551515615841. |
| [19] |
J.-L. Shih, C.-W. Chuang and G.-J. Hwang, An Inquiry-based Mobile Learning Approach to Enhancing Social Science Learning Effectiveness, Educational Technology & Society, 13 (2010), 50-62. |
| [20] |
P. Vakkari,
Searching as learning: A systematization based on literature, Journal of Information Science, 42 (2016), 7-18.
doi: 10.1177/0165551515615833. |
| [21] |
C. J. Van Rijsbergen, Information Retrieval (2nd ed.), Butterworth, 1979. |
| [22] |
A. Walraven, S. Brand-Gruwel and H. P. A. Boshuizen, How students evaluate information and sources when searching the World Wide, Web for information. Computers & Education, 52 (2009), 234-246. |
| [23] |
T. Willoughby, S. A. Anderson, E. Woodc, J. Mueller and C. Ross,
Fast searching for information on the Internet to use in a learning context: The impact of domain knowledge, Computers & Education, 52 (2009), 640-648.
doi: 10.1016/j.compedu.2008.11.009. |
| [24] |
C. Yin, H.-Y. Sung, G.-J. Hwang, S. Hirokawa, H.-C. Chu, B. Flanagan and Y. Tabata,
Learning by Searching: A Learning Environment that Provides Searching and Analysis Facilities for Supporting Trend Analysis Activities, Educational Technology & Society, 14 (2013), 1865-1889.
|
| [25] |
X. Zhang, J. Liu, C. Liu and M. Cole, Factors influencing users? perceived learning during online searching, in: Proceedings of the 9th International Conference on e-Learning (ICEL-2014), 2014, 200-210. |
| TREC topic # | Topic title keywords | MeSH category | Specificity |
| 2 | Generating transgenic mice | Genetic structure | Specific (4) |
| 7 | DNA repair and oxidative stress | Genetic processes | General (1) |
| 42 | Genes altered by chromosome translocations | Genetic phenomena | Specific (4) |
| 45 | Mental Health Wellness-1 | Genetic phenomena | General (1) |
| 49 | Glyphosate tolerance gene sequence | Genetic structure | General (1) |
| TREC topic # | Topic title keywords | MeSH category | Specificity |
| 2 | Generating transgenic mice | Genetic structure | Specific (4) |
| 7 | DNA repair and oxidative stress | Genetic processes | General (1) |
| 42 | Genes altered by chromosome translocations | Genetic phenomena | Specific (4) |
| 45 | Mental Health Wellness-1 | Genetic phenomena | General (1) |
| 49 | Glyphosate tolerance gene sequence | Genetic structure | General (1) |
Table 2.
Behavior Variables
| Behavior Variables | Description |
| # of Qs | The total number of queries submitted to the search system for a specific search task |
| q-Length | Query length is the number of words contained in a query. Here query length is the average length of multiple queries for a search task |
| # of Docs. saved | Number of documents/abstracts saved form the search results for a task |
| # of Docs. viewed | Number of documents/abstracts opened and viewed from the search results for a topic |
| Ratio-of-DocsSaved/Viewed | The ratio of documents saved and the documents opened/viewed |
| # of Actions task | The total number of actions during working on a search topic. The actions include both keyboard and mouse actions |
| # of SERPs viewed | Number of search result pages viewed or checked that were returned by the search system |
| Time for the Task | The total time spent on tasks |
| Ranking on SERPs | The average ranking position of the documents opened in SERPs. "1" is the top ranking, most related by the system and the larger the number, the lower the ranking is. |
| Average dwell time | Average time spent on viewing document/abstract |
| Querying time | Average time spent on working on queries |
| Behavior Variables | Description |
| # of Qs | The total number of queries submitted to the search system for a specific search task |
| q-Length | Query length is the number of words contained in a query. Here query length is the average length of multiple queries for a search task |
| # of Docs. saved | Number of documents/abstracts saved form the search results for a task |
| # of Docs. viewed | Number of documents/abstracts opened and viewed from the search results for a topic |
| Ratio-of-DocsSaved/Viewed | The ratio of documents saved and the documents opened/viewed |
| # of Actions task | The total number of actions during working on a search topic. The actions include both keyboard and mouse actions |
| # of SERPs viewed | Number of search result pages viewed or checked that were returned by the search system |
| Time for the Task | The total time spent on tasks |
| Ranking on SERPs | The average ranking position of the documents opened in SERPs. "1" is the top ranking, most related by the system and the larger the number, the lower the ranking is. |
| Average dwell time | Average time spent on viewing document/abstract |
| Querying time | Average time spent on working on queries |
Table 3.
Correlations between Perceived Learning and Behavior Measures
| Behavior Variables | Correlation with Perceived Learning ( |
|
| # of Qs | Correlation | -.085 |
| Sig. (2-tailed) | .320 | |
| q length | Correlation | .060 |
| Sig. (2-tailed) | .482 | |
| # of Docs Saved | Correlation | .1801 |
| Sig. (2-tailed) | .034 | |
| # of Docs opened or viewed | Correlation | .082 |
| Sig. (2-tailed) | .336 | |
| Ratio of Docs Saved or viewed | Correlation | .3112 |
| Sig. (2-tailed) | .000 | |
| # of Actions task | Correlation | .107 |
| Sig. (2-tailed) | .206 | |
| # of SERPs viewed | Correlation | .086 |
| Sig. (2-tailed) | .314 | |
| Time for task | Correlation | .031 |
| Sig. (2-tailed) | .714 | |
| Ranking on SERPs | Correlation | .1681 |
| Sig. (2-tailed) | .047 | |
| Average dwell time | Correlation | -.047 |
| Sig. (2-tailed) | .584 | |
| Query time | Correlation | -.049 |
| Sig. (2-tailed) | .567 | |
| 1Correlation is significant at the 0.05 level (2-tailed). 2Correlation is significant at the 0.01 level (2-tailed). |
||
| Behavior Variables | Correlation with Perceived Learning ( |
|
| # of Qs | Correlation | -.085 |
| Sig. (2-tailed) | .320 | |
| q length | Correlation | .060 |
| Sig. (2-tailed) | .482 | |
| # of Docs Saved | Correlation | .1801 |
| Sig. (2-tailed) | .034 | |
| # of Docs opened or viewed | Correlation | .082 |
| Sig. (2-tailed) | .336 | |
| Ratio of Docs Saved or viewed | Correlation | .3112 |
| Sig. (2-tailed) | .000 | |
| # of Actions task | Correlation | .107 |
| Sig. (2-tailed) | .206 | |
| # of SERPs viewed | Correlation | .086 |
| Sig. (2-tailed) | .314 | |
| Time for task | Correlation | .031 |
| Sig. (2-tailed) | .714 | |
| Ranking on SERPs | Correlation | .1681 |
| Sig. (2-tailed) | .047 | |
| Average dwell time | Correlation | -.047 |
| Sig. (2-tailed) | .584 | |
| Query time | Correlation | -.049 |
| Sig. (2-tailed) | .567 | |
| 1Correlation is significant at the 0.05 level (2-tailed). 2Correlation is significant at the 0.01 level (2-tailed). |
||
Table 4.
GLM/ANOVA Results of Effects of Behaviors on Perceived Learning
| Dependent Variable | Perceived Learning | ||||
| Source | Type Ⅲ Sum of Squares | df | Mean Square | F | Sig. |
| Corrected Model | 44.296a | 11 | 4.027 | 2.102 | .024 |
| Intercept | .221 | 1 | .221 | .115 | .735 |
| # of Qs | 3.299 | 1 | 3.299 | 1.722 | .192 |
| q length | .011 | 1 | 011 | .006 | .939 |
| # of Docs Saved | 1.068 | 1 | 1.068 | .558 | .457 |
| # of Docs opened viewed | 1.967 | 1 | 1.967 | 1.027 | .313 |
| Ratio of Docs Saved Viewed | 20.760 | 1 | 20.760 | 10.838 | .001 |
| #of Actions task | .309 | 1 | .309 | .161 | .688 |
| # of SERPs viewed | 1.426 | 1 | 1.426 | .745 | .390 |
| Time for Task | 2.610 | 1 | 2.610 | 1.362 | .245 |
| Ranking on SERPs | .505 | 1 | .505 | .264 | .608 |
| Average dwell time | 6.798 | 1 | 6.798 | 3.549 | .062 |
| Query time | 7.339 | 1 | 7.339 | 3.831 | .052 |
| Error | 245.175 | 128 | 1.915 | ||
| Total | 2434.500 | 140 | |||
| Corrected Total | 289.471 | 139 | |||
| aR Squared =.153 (Adjusted R Squared =.080) | |||||
| Dependent Variable | Perceived Learning | ||||
| Source | Type Ⅲ Sum of Squares | df | Mean Square | F | Sig. |
| Corrected Model | 44.296a | 11 | 4.027 | 2.102 | .024 |
| Intercept | .221 | 1 | .221 | .115 | .735 |
| # of Qs | 3.299 | 1 | 3.299 | 1.722 | .192 |
| q length | .011 | 1 | 011 | .006 | .939 |
| # of Docs Saved | 1.068 | 1 | 1.068 | .558 | .457 |
| # of Docs opened viewed | 1.967 | 1 | 1.967 | 1.027 | .313 |
| Ratio of Docs Saved Viewed | 20.760 | 1 | 20.760 | 10.838 | .001 |
| #of Actions task | .309 | 1 | .309 | .161 | .688 |
| # of SERPs viewed | 1.426 | 1 | 1.426 | .745 | .390 |
| Time for Task | 2.610 | 1 | 2.610 | 1.362 | .245 |
| Ranking on SERPs | .505 | 1 | .505 | .264 | .608 |
| Average dwell time | 6.798 | 1 | 6.798 | 3.549 | .062 |
| Query time | 7.339 | 1 | 7.339 | 3.831 | .052 |
| Error | 245.175 | 128 | 1.915 | ||
| Total | 2434.500 | 140 | |||
| Corrected Total | 289.471 | 139 | |||
| aR Squared =.153 (Adjusted R Squared =.080) | |||||
Table 5.
Correlations between Perceived Learning and Performance Measures
| Performance Measures | Correlation with Perceived Learning (n=140) | |
| Precision | Correlation | -.069 |
| Sig. (2-tailed) | .416 | |
| Recall | Correlation | .052 |
| Sig. (2-tailed) | .545 | |
| F2Score | Correlation | .047 |
| Sig. (2-tailed) | .579 | |
| Performance Measures | Correlation with Perceived Learning (n=140) | |
| Precision | Correlation | -.069 |
| Sig. (2-tailed) | .416 | |
| Recall | Correlation | .052 |
| Sig. (2-tailed) | .545 | |
| F2Score | Correlation | .047 |
| Sig. (2-tailed) | .579 | |
Table 6.
Correlations between Perceived Learning and Behavior measures for General and Specific Topics Separately
| Behavior Variables | Correlation with Perceived Learning | ||
| General Topics (n=90) | Specific Topics (n=50) | ||
| # of Qs | Correlation | -.085 | -.015 |
| Sig. (2-tailed) | .320 | .915 | |
| q length | Correlation | .045 | .084 |
| Sig. (2-tailed) | .671 | .563 | |
| # of Docs Saved | Correlation | .126 | .338 |
| Sig. (2-tailed) | .235 | .016 | |
| # of Docs opened or viewed | Correlation | .045 | .179 |
| Sig. (2-tailed) | .671 | .213 | |
| Ratio of Docs Saved or viewed | Correlation | .248 | .457 |
| Sig. (2-tailed) | .018 | .001 | |
| # of Actions task | Correlation | .058 | .0234 |
| Sig. (2-tailed) | .589 | .102 | |
| # of SERPs viewed | Correlation | .037 | .199 |
| Sig. (2-tailed) | .731 | .165 | |
| Time for task | Correlation | -.014 | .109 |
| Sig. (2-tailed) | .899 | .453 | |
| Ranking on SERPs | Correlation | .192 | .122 |
| Sig. (2-tailed) | .069 | .399 | |
| Average dwell time | Correlation | -.017 | -.105 |
| Sig. (2-tailed) | .871 | .469 | |
| Query time | Correlation | -.092 | .011 |
| Sig. (2-tailed) | .390 | .942 | |
| Behavior Variables | Correlation with Perceived Learning | ||
| General Topics (n=90) | Specific Topics (n=50) | ||
| # of Qs | Correlation | -.085 | -.015 |
| Sig. (2-tailed) | .320 | .915 | |
| q length | Correlation | .045 | .084 |
| Sig. (2-tailed) | .671 | .563 | |
| # of Docs Saved | Correlation | .126 | .338 |
| Sig. (2-tailed) | .235 | .016 | |
| # of Docs opened or viewed | Correlation | .045 | .179 |
| Sig. (2-tailed) | .671 | .213 | |
| Ratio of Docs Saved or viewed | Correlation | .248 | .457 |
| Sig. (2-tailed) | .018 | .001 | |
| # of Actions task | Correlation | .058 | .0234 |
| Sig. (2-tailed) | .589 | .102 | |
| # of SERPs viewed | Correlation | .037 | .199 |
| Sig. (2-tailed) | .731 | .165 | |
| Time for task | Correlation | -.014 | .109 |
| Sig. (2-tailed) | .899 | .453 | |
| Ranking on SERPs | Correlation | .192 | .122 |
| Sig. (2-tailed) | .069 | .399 | |
| Average dwell time | Correlation | -.017 | -.105 |
| Sig. (2-tailed) | .871 | .469 | |
| Query time | Correlation | -.092 | .011 |
| Sig. (2-tailed) | .390 | .942 | |
Table 7.
Correlations between Perceived Learning and Different Types of Topics
| Performance Measures | Perceived Learning | |
| General (n=90) | Specific (n=50) | |
| Precision | r=-.083, p=.439 | r=-.040, p=.785 |
| Recall | r=.021, p=.842 | r=.296, p=.037 |
| r=.015, p=.888 | r=.295, p=.037 | |
| Performance Measures | Perceived Learning | |
| General (n=90) | Specific (n=50) | |
| Precision | r=-.083, p=.439 | r=-.040, p=.785 |
| Recall | r=.021, p=.842 | r=.296, p=.037 |
| r=.015, p=.888 | r=.295, p=.037 | |
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