Journal of Educational Innovation Research 2018, Vol. 28, No. 3, pp.1-25 DOI: http://dx.doi.org/10.21024/pnuedi.28.3.201809.1 * An Analysis on Contents of the Pedagogy Examination for Secondary-School Teacher s Employment using Text Mining & Semantic Network Analysis Purpose: The purpose of this study is to identify the central contents and tendency of the pedagogy examination for secondary-school teacher s employment(2014~2018), to confirm the degree of agreement with preliminary teacher education standard and teacher education institution curriculum. Method: The main research methodologies used in this study are 'text mining' and 'semantic network analysis' methodology. The text mining methodology was used for morpheme analysis, key word extraction, frequency search, and 1 mode matrix generation. Semantic network analysis methodology was used to analyze the structural characteristics in language network(density, degree, centrality, etc.). Results: First, in this study, the key words (top 20 most frequently) in the annual pedagogy examination for secondary-school teachers' employment from year 2014 to 2018 are curriculum, education evaluation, educational methodology and educational technology, educational psychology, guidance and counseling. Especially, many key words seem to be core competencies that teachers should perform at school, such as class, evaluation, curriculum, motivation, and subject. Second, from the 2014 to the 2018 academic year, the semantic network characteristics of the top 30 frequency words in the entire pedagogy examination showed that the activity among the key words is high and the convergence between various key words is being performed. The most central, closest, and highly mediating key words in the pedagogy examination for secondary-school teachers' employment were students, classes, teachers, and interests. Conclusion & Suggestions: In this study, the content analysis of the national pedagogy examination for secondary-school teacher s employment was conducted objectively and profoundly. The results of this study are expected to be used as meaningful and positive data for the educational curriculum and education methods of the secondary-school teachers training. Key words : pedagogy examination for secondary-school teacher s employment, text mining, semantic network analysis, contents analysis * 2018 (KWUI 18-049). Corresponding Author: Kwon, Choong-Hoon. Kwangju Women s University, Dept. of Secondary Special Education, Yeodae-gil 201, Gwangsan-gu, Gwangju, Korea, e-mail: kwonch@kwu.ac.kr
.. (OECD, 2011). (Darling-Hammond, Holtzman, Gatlin, & Heilig, 2005).,,, (, 2010). (, 2017)..,... ( ) 2012 2 14.,,, 1. 6, 7, 17 2012 8 2, 2013 12 2014 (, 2012)., (,, 2011).,, -, -. Tyler(1949) Wheeler(1967) Nicholls Nicolls(1981).., -, -.
2014 (2013 ),... ( ) 2014 2018,. (KERIS) (RISS). 34. (33 )., 7, 5, 4, 3, / 2, / / / / / / / // 1. 2014 ( ),,, (2017) 1.,,, (2017) 2014-2017 6. 2014.. ( ) (,,, 2015)., (,, ).,,.. ( ), (Text) (,, 2013)... (BigData) (McKinsey Global Institution, 2011). (text mining) (semantic analysis) (, 2010; Leskovec, Rajaraman, & Ullman, 2016). (, ),,,.
,. (,,, 2011;, 2017), (,, 2015), (,, 2015;, 2016;, 2017).. (2014 2018),.., 2014,?, (,, )?.. ( ),,.. 2014 2018. () (2014) 2017 11 2018. 2014 2018 5. 2014 (), 2015 (), 7. (http://www.kice.re.kr) - PDF.
.,,, 1. (,, ). (text mining)., (Natural Language Processing) (, 2015). (statistic semantic hypothesis)., (Turney & Pantel, 2010). (Context).,,.. (, 2010). Loet Leydesdorff(2004) KrKwic(Korean Key Words in Context). KrKwic, (, 2014). KrKwic KrKwic, KrTitle, KrText 3 (). KrKwic (), 32bit 64bit KrWords. KrTitle () 1 (co-occurrence) (symmetric) (matrix). (wordcloud).
, (,, 2018; Gottron, 2009). (semantic network analysis), (,, 2012).,,. semantic network analysis (, 2016;, 2017). (contents analysis). (Text),, (, 2014).,, (, Loet Leydesdorff, 2004)., (,, 2013;,,, 2013)., (,, 2015). (node) (link). (node), (link).. (Local) (node) (degree). (Global) (Density), (Centrality) (,, 2012). Analytic Technology UCINET6 (ver. 6.645) NetDraw (ver. 2.161). UCINET6 KrKwic KrTitle 1,, 3 (,, ). NetDraw UCINET6 3 3.
,,.,,.,,,,. 5 ( ). ( ).,. 2014 (2013 ) 2018 (http://www.kice.re.kr). PDF. PDF HWP.,..,,, (, 2014)., (2013) (,, ). < -1>.,,,,,,,,, ( ) =>, =>, =>, =>, =>, => 2015,, =>2015,.. 1 (), 1 ( ), 1 ( )
4. (2 ) ( ).,., Windows Text (*.txt). Text KrKwic KrWords, Text wrdfrq.txt wrdfrq.dbf( 2 ) (, Loet Leydesdorff, 2004). (wrdfrq.dbf), 20. 20, 20.. R., R (http://wordcloud.kr).,. 7 Text. Text Krwords (), 30., KrTitle () 1 (coocc.dat coocc.dbf ). coocc.dbf Excel coocc.xlsx. UCINET6 coocc.xlsx UCINET coocc.##d, coocc.##h( 2 ). UCINET6 (,, ) 3 (,, ).,. 3 NetDraw. 30,. 3,,,. < -1>.
.. 1 1.. 2 2.. 1. 2014 2018., (wordcloud).,.. 20, ( ) ( ) < -1>.
17 7 5 4, 3, 2014 2014 ( ) 2015 2015 ( ),,,,,, 2,,,, 1 7 4 3, 2,,,,,,,,,,,, 1,, 11 8,,, 5, 4,,, 3,, 2,,,,, 9 8, 6,, 5,,, 4,,,,, 3,, 2
( ) 8 5, 4,,, 2016 3,,,,,, 2,,,, 1 2017 2018 8 2015, 6 5,, 4,, 3,,,,,, 2,,, 14 8 6 5,, 4,,, 3, 2 PBL,,,,,,, () 2014,,,,,. 2014 ( ),,,,,,,,,. 2015,,,,,,,. 2015 ( ),,,,,,,,. 2016,,,,,,,,. 2017 2015,,,,,,,,. 2018,,,,,,
,,. 2012 ( ) (, 2012). (Density) (Degree), (Centrality) (,, 2012;, 2011).. () ().. (Degree Centrality), (Closeness Centrality), (Betweenness Centrality) (,, 2016;,,, 2013;, 2014). 2., 2014 2018 ( 2, 7 ), 30. Analytic Technologies UCINET6 (ver. 6.645). NetDraw (ver. 2.161),. 1) 중등교사임용교육학시험핵심어전체언어네트워크 7 30 (nodes), (links) (edges). (Density), (Degree) (,, 2016; Scott, 2000). < -2>. 0.469 408 11.625 13.6 * : 0 1. n
n-1, n(n-1)/2 (,, 2016). 30, 30 29=(870 ). (408 ). 0.469, ( ) 13.6., (,,, 2013). (Node) (Degree) NetDraw < -1>..,,.,,,,,,,. () (Centrality), 2). 2) 중심성분석결과.
, (,, 2016; Drew, 2009; Scott, 2000)., (2016). (Freeman) 1979 3 (Degree Centrality), (Closeness Centrality), (Betweenness Centrality) (Freeman, 1979). 3,.,. 3,, (, 2014)., 3 (,, ),., (Degree Centrality) (links). (Local Centrality) (Freeman, 1979). UCINET6. < -3>. (Degree) (ndegree) (Degree) (ndegree) 734.000 0.202 28.000 0.008 441.000 0.122 40.000 0.011 433.000 0.119 33.000 0.009 138.000 0.038 69.000 0.019 82.000 0.023 54.000 0.015 69.000 0.019 99.000 0.027 224.000 0.062 19.000 0.005 268.000 0.074 174.000 0.48 49.000 0.014 191.000 0.053 2015 8.000 0.002 191.000 0.053 86.000 0.024 34.000 0.009 94.000 0.026 15.000 0.004 23.000 0.006 24.000 0.007
( ) (Degree) (ndegree) (Degree) (ndegree) 9.000 0.002 35.000 0.010 35.000 0.010 191.000 0.053 1) 10. 2) (ndegree) (n-1) (,, 2016 : 121-122). NetDraw. < -2>, (734.000), (441.000), (433.000), (268.000), (224.000), (191.000), (191.000), (191.000), (174.000) (138.000).., (Closeness Centrality) Closeness, (,, 2016).,.
(,, 2018)., (,,, 2013). UCINET6 3 (FreeClo., ValClo., RecipClo.). < -4>. FreeClo. ValClo. RecipClo. FreeClo. ValClo. RecipClo. 0.853 0.943 0.914 0.644 0.816 0.736 0.906 0.966 0.948 0.617 0.793 0.690 0.879 0.954 0.931 0.617 0.793 0.690 0.674 0.839 0.759 0.763 0.893 0.845 0.707 0.862 0.793 0.569 0.747 0.621 0.690 0.851 0.776 0.659 0.828 0.741 0.690 0.851 0.776 0.580 0.759 0.649 0.674 0.839 0.759 0.707 0.862 0.793 0.630 0.805 0.707 0.659 0.828 0.741 2015 0.558 0.736 0.603 0.659 0.828 0.741 0.744 0.885 0.828 0.644 0.816 0.724 0.725 0.874 0.810 0.468 0.2621 0.489 0.527 0.701 0.575 0.617 0.793 0.690 0.500 0.667.0523 0.569 0.747 0.632 0.659 0.828 0.741 0.659 0.828 0.741 1) (FreeClo. ) 10. 2) UCINET6 3. FreeClo. 1(Max observed distance plus 1), N-1. ValClo. ( Max. path length +1),. RecipClo. (,, 2016 : 122-123). NetDraw. < -3>, (0.906), (0.879), (0.853), (0.763), (0.744), (0.725), (0.707), (0.707), (0.690), (0.690).
, (Betweenness Centrality). Betweenness, (,, 2016)., (geodesic)., (,, 2016).,., (,,, 2013). UCINET6. < -5>. Betweenness nbetweenness Betweenness nbetweenness 48.535 11.954 4.241 1.045 37.996 9.359 1.848 0.455 40.815 10.053 1.603 0.395 9.680 2.384 9.925 2.445
( ) Betweenness nbetweenness Betweenness nbetweenness 20.399 5.024 0.350 0.086 10.687 2.632 1.516 0.373 4.965 1.223 0.091 0.022 1.860 0.458 6.412 1.579 2.969 0.731 0.960 0.236 2015 1.908 0.470 0.960 0.236 8.812 2.170 4.760 1.172 6.627 1.632 0.000 0.000 0.750 0.185 2.274 0.560 0.000 0.000 1.383 0.341 3.714 0.915 0.960 0.236 1) 10. 2) (nbetweenness).(,, 2016 : 123-124) NetDraw. < -4>, (48.535), (40.815), (37.996), (20.399), (10.687), (9.925), (9.680), (8.812), (6.627), (6.412) ( ).
. () 2014 2018 ( 7 ).,.,,., 2014 2018 ( 20 ).. 1., Loet Leydesdorff(2004) KrKwic(Korean Key Words in Context) (http://wordcloud.kr). 2016-106 (2016.12.23.)3 ( ) 10 2012-27(2012.11.21.) [ 2].,,,,,.,,,,,,,, 2015,.. 2016 2015. 2016, 2015, (, ) (, ),,,,..,,, (2017) 2014 2017.,,, (2017) KICE(2008) 2014 2017 6
..,,,,,.,., 2014 2018 7 30.. 2., Analytic Technologies UCINET6 NetDraw. 0.469, 408, 11.625, 13.6.,., ( ), (Centrality) 3 (,, ).,,,,,,,,,,, ( ) 5 7.,,.,, (2016).,, (2016),,,.,, ( ), ( ),..,,, (2017).,,, (2017) 6 (2014 2017) (Branch)
(Evaluation domain).. 2014 2018 7.,., (2017).,,.,, /, /,,,,,,,,..,,,,,,,,,.., 2014 2018 7,,,,,,,,,,,,,, 2015,,..,,,,,.,,. 3.,,,,,. 4 3 10.,,,,,,,,,. 7,..
, (2015).. (2), 185-211.,, (2013).. (2), 101-123. (2012). : (2012.02.14.). (2016). : 2015 (2016.08.18.). (2017). 2017. (2016).. (1), 125-148. doi: http://dx.doi.org/10.21024/pnuedi.26.1.201604.125., (2013). :. (2), 103-125., (2011). (4 ). :.,, (2015). (Semantic Network Analysis). (2), 449-471., (2016). (4 ). :.,, (2016).. (1), 183-209.,, (2011). (1991 2010). (2), 261-288.,, (2013). SNS -., 36-74. (2017). :. (3), 73-91., (2013). : (socio-cognitive network) ( ). (2), 73-108., Loet Leydesdorff (2004). KrKwic : Daum.net. Journal of the Korean Data Analysis Society, 6(5), 1377-1387., http://www.hanpark.net.
(2017). : 199 8 2015. (1), 131-143. (2012). 155, 2012.8.2. )., (2017).. (2), 365-395. doi: http://dx.doi.org/10.17232/kset.33.2.365.,,, (2017).. (4), 367-382., (2015). :. (5), 877-889. (2012). :. (2014).. (4), 49-68. (2015). :., (2018). :., (2012).. (2), 53-66. (2010).. (2011).. (4), 65-83., (2015). -1 2009 -. (4), 37-50. (2010). ( RR 2010-11). (2008).. http://www.kice.re.kr. (KERIS) (RISS). http://www.riss.kr. Darling-Hammond, L., Holtzman, D. J., Gatlin, S. J., & Heilig, J. V. (2005). Does teacher presentation matter? Evidence about teacher certification, teach for America, and teacher effectiveness. Education Policy Analysis Archives, 13(42), 1-51. Drew, M. (2009). Networking mapping. http://www.williemiller.co.uk/network-mapping.htm(cited 2009.9.20.). Freeman, L. (1979). Centrality in Social Networks Conceptual Classification. Social Networks, 1(3), 215-239.
Gottron, T. (2009). Document word clouds: Visualizing web documents as tag cloud to aid users in relevance decisions. Research and Advanced Technology for Digital Libraries-Lecture Notes in Computer Science, 5714, 94-105. Leskovec, J., Rajaraman, A., & Ullman, J. D. (2016). Mining of Massive Datasets(2nd ed.). Cambridge University Press. McKinsey Global Institution (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey & Company. Nicholls, A., & Nicholls, H. (1981). Developing a curriculum: A practical approach. George Allen & Unwin Ltd. OECD (2011). Teachers Matter: Attracting, Developing and Retaining Effective Teachers. Paris: OECD. Scott, J. (2000). Social Network Analysis. London: SAGE Publication. Turney, P. D., & Pantel, P. (2010). From frequency to meaning: Vector space models of semantics. Journal of artificial intelligence research, 37, 141-188. Tyler, R. W. (1949). Basic principles of curriculum and instruction. Chicago: The University of Chicago Press. Wheeler, D. K. (1967). Curriculum Process. London: University of London Press. : 2018.07.05. / : 2018.08.03. / : 2018.09.20.
: (2014 2018),. :.,,, 1. (,, ). :, 2014 2018 ( 20 ),,,,,.,,,,,., 2014 2018 30,. ( ), - ( ), ( ),,,. :..