A Framework for Characterizing Children's Statistical Thinking

Jones, Graham A.; Thornton, Carol A.; Langrall, Cynthia W.; Mooney, Edward S.; Perry, Bob; Putt, Ian

doi:10.1207/s15327833mtl0204_3

Cited by 82 publications

(77 citation statements)

References 21 publications

(34 reference statements)

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“…Ainley (2000) reported intuitive reading of graphs among 6-year-olds as an example of transparency of certain aspects of graphing, which comes quite close to some of our own previous research (e.g., Å berg- Bengtsson, 1998;Ottosson, 1987Ottosson, , 1988Ottosson & Å berg-Bengtsson, 1995), wherein the youngest elementary school pupils could handle particular features of commonly used graphs, charts, and maps quite adequately. Jones et al (2000) have, from a neo-Piagetian perspective, identified four ''thinking levels'' for statistical thinking among pupils in grades 1-5.…”

Section: Some Previous Researchmentioning

confidence: 99%

What lies behind graphicacy? Relating students' results on a test of graphically represented quantitative information to formal academic achievement

Åberg‐Bengtsson

Ottosson

2005

J Res Sci Teach

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Based on studies carried out on qualitative data an instrument was constructed for investigating how larger numbers of students handle graphics. This test, consisting of 18 pages, each with its own graphic display(s) and a set of tasks, was distributed to 363 students, 15-16 years of age, from five different schools. The format of the questions varied, as did the format of the graphics. As students' performance was expected to be multidimensional, confirmatory factor analysis was carried out with a structural equation modeling technique. In addition to the identification of a general graphicacy-test factor (Gen) and an end-of-test effect (End 0 ), a narrative dimension (Narr 0 ) was vaguely indicated. This model was then related to a six-factor model of students' formal academic achievement measured by their leaving certificates from compulsory education. The strongest correlation obtained was between the general graphicacy-test dimension (Gen) and a mathematic/science factor (MathSc 0 ) in the grades model. In addition, substantial relationships were detected between the Gen factor and both an overall school achievement factor (SchAch) and a language factor (Lang 0 ) in the grades model. ß 2005 Wiley Periodicals, Inc. J Res Sci Teach 43: [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62] 2006 Graphs, charts, cartograms, thematic maps, etc., are common tools for handling and communicating quantitative information in contemporary society. Successively, through their years of schooling, students encounter increasingly more advanced forms of graphic displays, both in direct educational situations and as illustrations and/or complementary facts on other subject matters. Students' access to modern computers equipped with graphic application software makes possible not only an abundance of graphics (sometimes unnecessarily elaborate) in magazines, newspapers, television, and electronic media-encountered in their everyday life both in and out of school-but also allows them to create these images. Thus, being ''graphicate''

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Section: Some Previous Researchmentioning

confidence: 99%

What lies behind graphicacy? Relating students' results on a test of graphically represented quantitative information to formal academic achievement

Åberg‐Bengtsson

Ottosson

2005

J Res Sci Teach

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“…Merujuk pada pendapat Jones, et al (2000), delMas (2002), Chance (2002), serta Ben-Zvi & Friedlander (2010), kemampuan berpikir statistis diartikan sebagai kemampuan untuk mengerti dan memahami bagaimana cara mendeskripsikan data, mengorganisasikan data, merepresentasikan data, menganalisis dan menginterpretasikan data, serta kemampuan untuk mengaplikasikan pemahaman statistis pada masalah nyata dengan cara memberikan kritik, evaluasi, dan membuat generalisasi. Selanjutnya berdasarkan tahap berpikir kognitif model Structure of the Observed Learning Outcome (SOLO), Jones, et al (2000) membagi tingkatan kemampuan berpikir statistis dalam empat level, yaitu Idiosyncratic, Transitional, Quantitative, dan Analytical. Tingkat terendah dari kemampuan berpikir statistis disebut level Idiosyncratic yang ditandai dengan munculnya kemampuan berpikir prestruktural tetapi terganggu atau dikacaukan oleh aspek-aspek yang tidak relevan.…”

Section: Pendahuluanunclassified

“…Selanjutnya berdasarkan tahap berpikir kognitif model Structure of the Observed Learning Outcome (SOLO), Jones, et al (2000) membagi tingkatan kemampuan berpikir statistis dalam empat level, yaitu Idiosyncratic, Transitional, Quantitative, dan Analytical. Tingkat terendah dari kemampuan berpikir statistis disebut level Idiosyncratic yang ditandai dengan munculnya kemampuan berpikir prestruktural tetapi terganggu atau dikacaukan oleh aspek-aspek yang tidak relevan. Tingkatan selanjutnya disebut level Transitional yaitu seseorang mencoba untuk menyajikan gagasannya melalui berpikir kuantatif tetapi secara umum perhatiannya tertuju hanya pada satu aspek dari data, kadangkadang mundur ke level Idiosyncratic.…”

Section: Pendahuluanunclassified

MODIFIKASI MEAs DENGAN MENGGUNAKAN DIDACTICAL DESIGN RESEARCH UNTUK MENINGKATKAN KEMAMPUAN BERPIKIR STATISTIS MAHASISWA

cakrawala¹

2014

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Abstrak: Artikel ini berisi hasil penelitian tentang bagaimana cara memodifikasi pembelajaran ModelEliciting Activities (MEAs) dengan menggunakan Didactical Design Research (DDR). Metode yang digunakan adalah Research & Development melalui tahap-tahap: persiapan untuk mengetahui efektivitas bahan ajar, pembelajaran MEAs yang dimodifikasi, tingkat validitas, reliabilitas, daya pembeda, dan indeks kesukaran instrumen tes; pelaksanaan penelitian berupa uji coba pembelajaran MEAs yang dimodifikasi terhadap seluruh mahasiswa S1 pendidikan matematika yang mengikuti mata kuliah Statistika Dasar pada semester genap TA. 2011/2012 di sebuah PTN di Kota Bandung dengan metode kuasi-eksperimen menggunakan Split-Plot Nested Design. Hasil penelitian menunjukkan bahwa peningkatan kemampuan berpikir statistis mahasiswa reguler dan mahasiswa mengulang yang memperoleh pembelajaran MEAs yang dimodifikasi lebih tinggi secara signifikan dari mahasiswa yang memperoleh pembelajaran konvensional.Kata Kunci: Model-Eliciting Activities (MEAs) yang dimodifikasi, didactical disain research MODIFICATION OF THE MEAs USING THE DIDACTICAL DESIGN RESEARCH TO IMPROVE THE STUDENTS' STATISTICAL REASONING ABILITYAbstract: This paper contains the findings of the study on how to modify the Model-ElicitngActivities (MEAs) teaching using the Didactical Design Research (DDR). The method used was the research and development through the following steps: preparation to find out the effectiveness of the teaching materials, modified MEAs teaching, the validity level, reliability, the discrimination level, and the test instrument difficulty index; the research implementation in the form of piloting the modified MEAs teaching to the undergraduate students of the Mathematics education program joining the Elementary Statistics course in the second semester of the 2011/2012 year in a state university in Bandung using the quasi-experiment with the Split-Plot Nested Design. The findings showed that the improvement of the statistical reasoning of the regular students and the repeaters obtaining the modified MEAs teaching was significantly higher than that of those obtaining the conventional teaching.

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“…This study is based on a Statistical Thinking Framework (Framework) (Jones et al, 2000) that characterises children's statistical thinking according to four cognitive levels and across four key constructs: describing data, organising and reducing data, representing data, and analysing and interpreting data. The present study is concerned with two of the constructs (a) representing data, and (b) organising and reducing data.…”

Section: Theoretical Considerationsmentioning

confidence: 99%