Self-explanation in the domain of s

Self-explanation in the domain of statistics: an expertise
reversal effect
Jimmie Leppink • Nick J. Broers • Tjaart Imbos •
Cees P. M. van der Vleuten • Martijn P. F. Berger
Published online: 4 September 2011
The Author(s) 2011. This article is published with open access at Springerlink.com
Abstract This study investigated the effects of four instructional methods on cognitive
load, propositional knowledge, and conceptual understanding of statistics, for low prior
knowledge students and for high prior knowledge students. The instructional methods were
(1) a reading-only control condition, (2) answering open-ended questions, (3) answering
open-ended questions and formulating arguments, and (4) studying worked-out examples
of the type of arguments students in the third group had to formulate themselves. The
results indicate that high prior knowledge students develop more propositional knowledge
of statistics than low prior knowledge students. With regard to conceptual understanding,
the results indicate an expertise reversal effect: low prior knowledge students learn most
from studying worked-out examples, whereas high prior knowledge students profit most
from formulating arguments. Thus, novice students should be guided into the subject
matter by means of worked-out examples. As soon as students have developed more
knowledge of the subject matter, they should be provided with learning tasks that stimulate
students to solve problems by formulating arguments.
Keywords Cognitive load Propositional knowledge Conceptual understanding
Expertise reversal effect
Introduction
The statistics knowledge domain is known for its abstract and cumulative nature. Although
students usually develop knowledge of statistical principles and definitions (i.e., propositional
knowledge, Broers 2002) they frequently lack the ability to structure their

Self-explanation in the domain of statistics: an expertise
reversal effect
Jimmie Leppink • Nick J. Broers • Tjaart Imbos •
Cees P. M. van der Vleuten • Martijn P. F. Berger
Published online: 4 September 2011
 The Author(s) 2011. This article is published with open access at Springerlink.com
Abstract This study investigated the effects of four instructional methods on cognitive
load, propositional knowledge, and conceptual understanding of statistics, for low prior
knowledge students and for high prior knowledge students. The instructional methods were
(1) a reading-only control condition, (2) answering open-ended questions, (3) answering
open-ended questions and formulating arguments, and (4) studying worked-out examples
of the type of arguments students in the third group had to formulate themselves. The
results indicate that high prior knowledge students develop more propositional knowledge
of statistics than low prior knowledge students. With regard to conceptual understanding,
the results indicate an expertise reversal effect: low prior knowledge students learn most
from studying worked-out examples, whereas high prior knowledge students profit most
from formulating arguments. Thus, novice students should be guided into the subject
matter by means of worked-out examples. As soon as students have developed more
knowledge of the subject matter, they should be provided with learning tasks that stimulate
students to solve problems by formulating arguments.
Keywords Cognitive load  Propositional knowledge  Conceptual understanding 
Expertise reversal effect
Introduction
The statistics knowledge domain is known for its abstract and cumulative nature. Although
students usually develop knowledge of statistical principles and definitions (i.e., propositional
knowledge, Broers 2002) they frequently lack the ability to structure their

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Self-explanation in the domain of statistics: an expertisereversal effectJimmie Leppink • Nick J. Broers • Tjaart Imbos •Cees P. M. van der Vleuten • Martijn P. F. BergerPublished online: 4 September 2011 The Author(s) 2011. This article is published with open access at Springerlink.comAbstract This study investigated the effects of four instructional methods on cognitiveload, propositional knowledge, and conceptual understanding of statistics, for low priorknowledge students and for high prior knowledge students. The instructional methods were(1) a reading-only control condition, (2) answering open-ended questions, (3) answeringopen-ended questions and formulating arguments, and (4) studying worked-out examplesof the type of arguments students in the third group had to formulate themselves. Theresults indicate that high prior knowledge students develop more propositional knowledgeof statistics than low prior knowledge students. With regard to conceptual understanding,the results indicate an expertise reversal effect: low prior knowledge students learn mostfrom studying worked-out examples, whereas high prior knowledge students profit mostfrom formulating arguments. Thus, novice students should be guided into the subjectmatter by means of worked-out examples. As soon as students have developed moreknowledge of the subject matter, they should be provided with learning tasks that stimulatestudents to solve problems by formulating arguments.Keywords Cognitive load Propositional knowledge Conceptual understanding Expertise reversal effectIntroductionThe statistics knowledge domain is known for its abstract and cumulative nature. Althoughstudents usually develop knowledge of statistical principles and definitions (i.e., propositionalknowledge, Broers 2002) they frequently lack the ability to structure their

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Self-penjelasan dalam domain statistik: keahlian
efek pembalikan
Jimmie Leppink • Nick J. Broers • Tjaart Imbos •
Cees PM van der Vleuten • Martijn PF Berger
Diterbitkan online: 4 September 2011
? Penulis (s) 2011. Artikel ini diterbitkan dengan akses terbuka di Springerlink.com
Abstrak Penelitian ini meneliti efek dari empat metode pembelajaran pada kognitif
beban, pengetahuan proposisional, dan pemahaman konseptual statistik, untuk sebelum rendah
siswa pengetahuan dan untuk tinggi sebelum siswa pengetahuan. Metode pembelajaran yang
(1) kondisi kontrol membaca-satunya, (2) menjawab pertanyaan-pertanyaan terbuka, (3) menjawab
pertanyaan-pertanyaan terbuka dan merumuskan argumen, dan (4) belajar bekerja-out contoh
dari jenis siswa argumen di kelompok ketiga harus merumuskan sendiri. The
Hasil penelitian menunjukkan bahwa tinggi siswa pengetahuan sebelumnya mengembangkan pengetahuan lebih proposisional
statistik dari siswa pengetahuan sebelumnya rendah. Berkenaan dengan pemahaman konseptual,
hasil menunjukkan efek pembalikan keahlian: rendah siswa pengetahuan sebelumnya belajar paling
dari belajar bekerja-out contoh, sedangkan tinggi siswa pengetahuan keuntungan paling
dari argumen merumuskan. Dengan demikian, siswa pemula harus dibimbing menjadi subjek
materi dengan cara contoh bekerja-out. Begitu siswa telah mengembangkan lebih
pengetahuan tentang materi pelajaran, mereka harus diberikan dengan tugas-tugas belajar yang merangsang
siswa untuk memecahkan masalah dengan merumuskan argumen.
Kata kunci beban kognitif? Pengetahuan proposisional? Konseptual pemahaman?
Efek Keahlian reversal
Pengantar
Statistik domain pengetahuan dikenal karena sifat abstrak dan kumulatifnya. Meskipun
siswa biasanya mengembangkan pengetahuan tentang prinsip-prinsip statistik dan definisi (yaitu, proposisi
pengetahuan, Broers 2002) mereka sering tidak memiliki kemampuan untuk struktur mereka

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