Can computational talent be detected? Predictive validity of the Computational Thinking Test
Tài liệu tham khảo
Manovich, 2013
Rushkoff, 2010
M. Prensky, Programming is the new literacy, Edutopia, 2008. https://www.edutopia.org/literacy-computer-programming.
D. Rushkoff, Code literacy: A 21st-century requirement, Edutopia, 2012. https://www.edutopia.org/blog/code-literacy-21st-century-requirement-douglas-rushkoff.
Román-González, 2014, Aprender a programar “apps” como enriquecimiento curricular en alumnado de alta capacidad [To learn programming “apps” as curriculum enrichment on gifted students], Bordón. Rev. Pedagog., 66, 135
Lye, 2014, Review on teaching and learning of computational thinking through programming: What is next for K-12?, Comput. Hum. Behav., 41, 51, 10.1016/j.chb.2014.09.012
S. Grover, S. Cooper, R. Pea, Assessing computational learning in K-12, in: Proc. 2014 Conf. Innov. Technol. Comput. Sci. Educ., 2014, pp. 57–62. http://dx.doi.org/10.1145/2591708.2591713.
Wing, 2006, Computational thinking, Commun. ACM, 49, 33, 10.1145/1118178.1118215
Wing, 2008, Computational thinking and thinking about computing, Phil. Trans. R. Soc. A, 366, 3717, 10.1098/rsta.2008.0118
A. Balanskat, K. Engelhardt, Computing our future: Computer programming and coding-Priorities, school curricula and initiatives across Europe, European Schoolnet, Brussels, 2014. http://www.eun.org/c/document_library/get_file?uuid=521cb928-6ec4-4a86-b522-9d8fd5cf60ce&groupId=43887.
S. Bocconi, A. Chioccariello, G. Dettori, A. Ferrari, K. Engelhardt, et al., Developing Computational Thinking in Compulsory Education-Implications for policy and practice, Join Research Center (European Commission), Seville, 2016. http://publications.jrc.ec.europa.eu/repository/bitstream/JRC104188/jrc104188_computhinkreport.pdf .
Grover, 2013, Computational thinking in K–12 a review of the state of the field, Educ. Res., 42, 38, 10.3102/0013189X12463051
Kalelioglu, 2016, A framework for computational thinking based on a systematic research review, Balt. J. Mod. Comput., 4, 583
Shute, 2017, Demystifying computational thinking, Educ. Res. Rev., 22, 142, 10.1016/j.edurev.2017.09.003
A. Mühling, A. Ruf, P. Hubwieser, Design and first results of a psychometric test for measuring basic programming abilities, in: Proc. Work. Prim. Second. Comput. Educ., 2015, pp. 2–10. http://dx.doi.org/10.1145/2818314.2818320.
P.S. Buffum, E.V. Lobene, M.H. Frankosky, K.E. Boyer, E.N. Wiebe, J.C. Lester, A practical guide to developing and validating computer science knowledge assessments with application to middle school, in: Proc. 46th ACM Tech. Symp. Comput. Sci. Educ., 2015, pp. 622–627. http://dx.doi.org/10.1145/2676723.2677295.
Román-González, 2017, Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test, Comput. Hum. Behav., 72, 678, 10.1016/j.chb.2016.08.047
Aho, 2012, Computation and computational thinking, Comput. J., 55, 832, 10.1093/comjnl/bxs074
B. Rodriguez, S. Kennicutt, C. Rader, T. Camp, Assessing computational thinking in CS unplugged activities, in: Proc. 2017 ACM SIGCSE Tech. Symp. Comput. Sci. Educ., 2017, pp. 501–506. http://dx.doi.org/10.1145/3017680.3017779.
Brackmann, 2017, Development of computational thinking skills through unplugged activities in primary school, 65
M. Román-González, Computational thinking test: Design guidelines and content validation, in: Proc. 7th Annu. Int. Conf. Educ. New Learn. Technol. (EDULEARN 2015), 2015, pp. 2436–2444. http://dx.doi.org/10.13140/RG.2.1.4203.4329.
Román-González, 2016, Does computational thinking correlate with personality?: The non-cognitive side of computational thinking, 51
Román-González, 2018, Extending the nomological network of computational thinking with non-cognitive factors, Comput. Hum. Behav., 80, 441, 10.1016/j.chb.2017.09.030
Best, 2011, Relations between executive function and academic achievement from ages 5 to 17 in a large, representative national sample, Learn. Individ. Differ., 21, 327, 10.1016/j.lindif.2011.01.007
Finn, 2014, Cognitive skills, student achievement tests, and schools, Psychol. Sci., 25, 736, 10.1177/0956797613516008
Gagné, 2001, When IQ is controlled, does motivation still predict achievement?, Intelligence, 30, 71, 10.1016/S0160-2896(01)00068-X
Laidra, 2007, Personality and intelligence as predictors of academic achievement: A cross-sectional study from elementary to secondary school, Pers. Individ. Differ., 42, 441, 10.1016/j.paid.2006.08.001
Gottschling, 2012, The prediction of school achievement from a behavior genetic perspective: Results from the German twin study on Cognitive Ability, Self-Reported Motivation, and School Achievement (CoSMoS), Pers. Individ. Differ., 53, 381, 10.1016/j.paid.2012.01.020
Jensen, 1989, The relationship between learning and intelligence, Learn. Individ. Differ., 1, 37, 10.1016/1041-6080(89)90009-5
Kaufman, 2012, Are cognitive g and academic achievement g one and the same g? An exploration on the Woodcock–Johnson and Kaufman tests, Intelligence, 40, 123, 10.1016/j.intell.2012.01.009
Eaves, 1990, Cognition and academic achievement: The relationship of the cognitive levels test, the keymath revised, and the woodcock reading mastery tests-revised, Psychol. Sch., 27, 311, 10.1002/1520-6807(199010)27:4<311::AID-PITS2310270406>3.0.CO;2-K
Fleming, 1983, The relationship of student characteristics and student performance in science as viewed by meta-analysis research, J. Res. Sci. Teach., 20, 481, 10.1002/tea.3660200510
Lieury, 2016, Video games vs. reading and school/cognitive performances: a study on 27000 middle school teenagers, Educ. Psychol., 36, 1560, 10.1080/01443410.2014.923556
Koubek, 1985, Predicting performance in computer programming courses, Behav. Inf. Technol., 4, 113, 10.1080/01449298508901793
Cafolla, 1987, Piagetian formal operations and other cognitive correlates of achievement in computer programming, J. Educ. Technol. Syst., 16, 45, 10.2190/GW1P-7BBK-DVWR-W3TU
Shute, 1991, Who is likely to acquire programming skills?, J. Educ. Comput. Res., 7, 1, 10.2190/VQJD-T1YD-5WVB-RYPJ
S. Fincher, A. Robins, B. Baker, I. Box, Q. Cutts, M. de Raadt, P. Haden, J. Hamer, M. Hamilton, R. Lister, et al., Predictors of success in a first programming course, in: Proc. 8th Australas. Conf. Comput. Educ. Vol. 52, 2006, pp. 189–196. https://dl.acm.org/citation.cfm?id=1151894.
Cegielski, 2006, What makes a good programmer?, Commun. ACM, 49, 73, 10.1145/1164394.1164397
Erdogan, 2008, Exploring the psychological predictors of programming achievement, J. Instr. Psychol., 35, 264
Davis, 1989
Brown, 2005, Assumptions underlying the identification of gifted and talented students, Gift. Child Q., 49, 68, 10.1177/001698620504900107
Lubinski, 2016, From terman to today: A century of findings on intellectual precocity, Rev. Educ. Res., 86, 900, 10.3102/0034654316675476
Baum, 1996, Talent beyond words: Identification of potential talent in dance and music in elementary students, Gift. Child Q., 40, 93, 10.1177/001698629604000206
Koshy, 2009, Mathematically gifted and talented learners: theory and practice, Internat. J. Math. Ed. Sci. Tech., 40, 213, 10.1080/00207390802566907
Maker, 1996, Identification of gifted minority students: A national problem, needed changes and a promising solution, Gift. Child Q., 40, 41, 10.1177/001698629604000106
Richert, 1987, Rampant problems and promising practices in the identification of disadvantaged gifted students, Gift. Child Q., 31, 149, 10.1177/001698628703100403
Anvari, 2013, Using cognitive load measurement and spatial ability test to identify talented students in three-dimensional computer graphics programming, Int. J. Inf. Educ. Technol., 3, 94
F. Anvari, D. Richards, Using personality traits and a spatial ability test to identify talented aspiring designers in User-Centred Design methodologies, in: Eval. Nov. Approaches to Softw. Eng. (ENASE), 2015 Int. Conf., 2015, pp. 90–101. http://ieeexplore.ieee.org/document/7320341/.
F. Anvari, D. Richards, A method to identify talented aspiring designers in use of personas with personality, in: Int. Conf. Eval. Nov. Approaches to Softw. Eng., 2015, pp. 40–61. http://dx.doi.org/10.1007/978-3-319-30243-0_3.
Lerner, 2002, Some simple economics of open source, J. Ind. Econ., 50, 197, 10.1111/1467-6451.00174
D. Weintrop, Blocks, text, and the space between: The role of representations in novice programming environments, in: Vis. Lang. Human-Centric Comput. (VL/HCC), 2015 IEEE Symp., 2015, pp. 301–302. http://dx.doi.org/10.1109/VLHCC.2015.7357237.
D. Weintrop, U. Wilensky, To block or not to block, that is the question: students’ perceptions of blocks-based programming, in: Proc. 14th Int. Conf. Interact. Des. Child., 2015, pp. 199–208. http://dx.doi.org/10.1145/2771839.2771860.
M. Kölling, N.C.C. Brown, A. Altadmri, Frame-based editing: Easing the transition from blocks to text-based programming, in: Proc. Work. Prim. Second. Comput. Educ., 2015, pp. 29–38. http://dx.doi.org/10.1145/2818314.2818331.
Armoni, 2015, From Scratch to “real” programming, ACM Trans. Comput. Educ., 14, 25:1, 10.1145/2677087
Kulik, 1984, Effects of accelerated instruction on students, Rev. Educ. Res., 54, 409, 10.3102/00346543054003409
Steenbergen-Hu, 2016, What one hundred years of research says about the effects of ability grouping and acceleration on K–12 students’ academic achievement: Findings of two second-order meta-analyses, Rev. Educ. Res., 86, 849, 10.3102/0034654316675417
M. Stephen, I. Warwick, Educating the More Able Student: What Works and why, SAGE, 2015, http://dx.doi.org/10.4135/9781473922204.
Maggio, 2013, Trying out acceleration for mathematically talented fifth graders, Gift. Child Today, 36, 20, 10.1177/1076217512465284
Wai, 2010, Accomplishment in science, technology, engineering, and mathematics (STEM) and its relation to STEM educational dose: A 25-year longitudinal study, J. Educ. Psychol., 102, 860, 10.1037/a0019454
U.K. Department of Education, National Curriculum in England: Computing Programmes of Study, 2013. https://www.gov.uk/government/publications/national-curriculum-in-england-computing-programmes-of-study.
K. Falkner, R. Vivian, N. Falkner, The Australian digital technologies curriculum: challenge and opportunity, in: Proc. Sixt. Australas. Comput. Educ. Conf. Vol. 148, 2014, pp. 3–12. https://dl.acm.org/citation.cfm?id=2667491.
D. Seehorn, S. Carey, B. Fuschetto, I. Lee, D. Moix, D. O’Grady-Cunniff, B.B. Owens, C. Stephenson, A. Verno, CSTA K–12 Computer Science Standards: Revised 2011, 2011. http://c.ymcdn.com/sites/www.csteachers.org/resource/resmgr/Docs/Standards/CSTA_K-12_CSS.pdf.
Berrocoso, 2015, El pensamiento computacional y las nuevas ecologías del aprendizaje, Rev. Educ. A Distancia
Howland, 2015, Learning to communicate computationally with Flip: A bi-modal programming language for game creation, Comput. Educ., 80, 224, 10.1016/j.compedu.2014.08.014
Good, 2016, Programming language, natural language? Supporting the diverse computational activities of novice programmers, J. Vis. Lang. Comput.
Association, 1999
Román-González, 2017, Complementary tools for computational thinking assessment, 154
Moreno-León, 2015, Dr. Scratch: automatic analysis of scratch projects to assess and foster computational thinking, RED. Rev. Educ. A Distancia, 15, 1
K.H. Koh, A. Basawapatna, V. Bennett, A. Repenning, Towards the automatic recognition of computational thinking for adaptive visual language learning, in: Vis. Lang. Human-Centric Comput. (VL/HCC), 2010 IEEE Symp., 2010, pp. 59–66. http://dx.doi.org/10.1109/VLHCC.2010.17.
Dagiene, 2008, Bebras international contest on informatics and computer literacy: Criteria for good tasks, 19
A. Basawapatna, K.H. Koh, A. Repenning, D.C. Webb, K.S. Marshall, Recognizing computational thinking patterns, in: Proc. 42nd ACM Tech. Symp. Comput. Sci. Educ., 2011, pp. 245–250. http://dx.doi.org/10.1145/1953163.1953241.
Grover, 2017, A framework for using hypothesis-driven approaches to support data-driven learning analytics in measuring computational thinking in block-based programming environments, ACM Trans. Comput. Educ., 17, 14:1, 10.1145/3105910
Korkmaz, 2017, A validity and reliability study of the Computational Thinking Scales (CTS), Comput. Hum. Behav., 10.1016/j.chb.2017.01.005
S. Grover, Robotics and engineering for middle and high school students to develop computational thinking, in: Annu. Meet. Am. Educ. Res. Assoc. New Orleans, LA, 2011. https://pdfs.semanticscholar.org/69a7/c5909726eed5bd66719aad69565ce46bbdcc.pdf .
Cohen, 1992, A power primer, Psychol. Bull., 112, 155, 10.1037/0033-2909.112.1.155
Hanley, 1982, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 29, 10.1148/radiology.143.1.7063747
Jacobs, 1994, Gender stereotypes: Implications for gifted education, Roeper Rev., 16, 152, 10.1080/02783199409553562
Swiatek, 1991, Ten-year longitudinal follow-up of ability-matched accelerated and unaccelerated gifted students, J. Educ. Psychol., 83, 528, 10.1037/0022-0663.83.4.528
Kretschmann, 2016, Skipping to the bigger pond: Examining gender differences in students’ psychosocial development after early acceleration, Contemp. Educ. Psychol., 46, 195, 10.1016/j.cedpsych.2016.06.001
N. Seisdedos, RP-30: Test de Resolución de problemas. Manual Técnico [RP-30: Problem-Solving Test. Technical Manual], TEA Ediciones, 2002.
K. Brennan, M. Resnick, New frameworks for studying and assessing the development of computational thinking, in: Proc. 2012 Annu. Meet. Am. Educ. Res. Assoc. Vancouver, Canada, 2012, pp. 1–25. http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf.
L.L. Werner, J. Denner, S. Bean, Pair programming strategies for middle school girls, in: CATE, 2004, pp. 161–166. https://pdfs.semanticscholar.org/c143/ef2b9060bcd125f959bbb34223a29d970bef.pdf.
Werner, 2009, Pair programming in middle school: What does it look like?, J. Res. Technol. Educ., 42, 29, 10.1080/15391523.2009.10782540
M.S. Horn, E.T. Solovey, R.J. Crouser, R.J.K. Jacob, Comparing the use of tangible and graphical programming languages for informal science education, in: Proc. SIGCHI Conf. Hum. Factors Comput. Syst., 2009, pp. 975–984. http://dx.doi.org/10.1145/1518701.1518851.
S.B. Daily, A.E. Leonard, S. Jörg, S. Babu, K. Gundersen, Dancing alice: Exploring embodied pedagogical strategies for learning computational thinking, in: Proc. 45th ACM Tech. Symp. Comput. Sci. Educ., 2014, pp. 91–96. http://dx.doi.org/10.1145/2538862.2538917.
Benotti, 2018, A tool for introducing computer science with automatic formative assessment, IEEE Trans. Learn. Technol., 11, 179, 10.1109/TLT.2017.2682084
L. Benotti, M.C. Martínez, F. Schapachnik, Engaging high school students using chatbots, in: Proc. 2014 Conf. Innov. Technol. Comput. Sci. Educ., 2014, pp. 63–68. http://dx.doi.org/10.1145/2591708.2591728.