ArticlePDF Available

Valid Issues but Limited Scope: A Response to Kitchen and Berk's Research Commentary on Educational Technology

Authors:

Abstract

In their Research Commentary, Kitchen and Berk (2016) argue that educational technology may focus only on skills for low-income students and students of color, further limiting their opportunities to learn mathematical reasoning, and thus pose a challenge to realizing standards-based reforms. Although the authors share the concern about equity and about funds wasted by inappropriate purchases of technology before planning based on research and the wisdom of expert practice, including inadequate professional development, they believe that Kitchen and Berk's commentary contains several limitations that could be misconstrued and thus misdirect policy and practice.
Valid Issues but Limited Scope: A Response to Kitchen and Berk's Research Commentary on
Educational Technology
Author(s): Douglas H. Clements and Julie Sarama
Source:
Journal for Research in Mathematics Education,
Vol. 48, No. 5 (November 2017),
pp. 474-482
Published by: National Council of Teachers of Mathematics
Stable URL: http://www.jstor.org/stable/10.5951/jresematheduc.48.5.0474
Accessed: 01-11-2017 18:02 UTC
REFERENCES
Linked references are available on JSTOR for this article:
http://www.jstor.org/stable/10.5951/jresematheduc.48.5.0474?seq=1&cid=pdf-
reference#references_tab_contents
You may need to log in to JSTOR to access the linked references.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide
range of content in a trusted digital archive. We use information technology and tools to increase productivity and
facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at
http://about.jstor.org/terms
National Council of Teachers of Mathematics
is collaborating with JSTOR to digitize,
preserve and extend access to
Journal for Research in Mathematics Education
This content downloaded from 128.138.154.222 on Wed, 01 Nov 2017 18:02:11 UTC
All use subject to http://about.jstor.org/terms
Research Commentary
Valid Issues but Limited Scope:
A Response to Kitchen and Berk’s Research
Commentary on Educational Technology
Douglas H. Clements and Julie Sarama
University of Denver
In their Research Commentary, Kitchen and Berk (2016) argue that educational tech-
nology may focus only on skills for low-income students and students of color, further
limiting their opportunities to learn mathematical reasoning, and thus pose a challenge
to realizing standards-based reforms. Although we share the concern about equity and
about funds wasted by inappropriate purchases of technology before planning based
on research and the wisdom of expert practice, including inadequate professional
development, we believe that Kitchen and Berk’s commentary contains several limi-
tations that could be misconstrued and thus misdirect policy and practice.
Keywords: Educational technology; Equity; Mathematics education
In their Research Commentary, Kitchen and Berk (2016) argue that educational
technology may focus only on skills for low-income students and students of color,
further limiting their opportunities to learn mathematical reasoning, and thus pose
a challenge to realizing standards-based reforms reflected most recently in the
Common Core State Standards for Mathematics (CCSSM; National Governors
Association [NGA] Center for Best Practices & Council of Chief State School
Officers [CCSSO], 2010). As mathematics educators and researchers in this area,
we share the concern about funds wasted by inappropriate purchases of technology
before planning based on research and the wisdom of expert practice, including
inadequate professional development (Clements & Sarama, 2003). However, we
believe that Kitchen and Berk’s commentary contains several limitations and
interpretations that could be misconstrued and thus misdirect policy and practice.
Affirming the many shared concerns, in this commentary we focus on issues
related to those limitations.
Our critique addresses the following five issues: (a) a focus on the technology
per se rather than the specific content and pedagogy of computer interventions,
(b) generalization to all educational applications of computers after raising
Journal for Research in Mathematics Education
2017, Vol. 48, No. 5, 474–482
This research was supported by the Institute of Education Sciences, U.S.
Department of Education through Grants R305K05157 and R305A120813 and
the National Science Foundation through Grant DRL-1313695. The opinions
expressed are those of the authors and do not represent the views of the U.S.
Department of Education or the National Science Foundation.
Copyright © 2017 by the National Council of Teachers of Mathematics, Inc., www.nctm.org. All rights reserved.
This material may not be copied or distributed electronically or in other formats without written permission from NCTM.
This content downloaded from 128.138.154.222 on Wed, 01 Nov 2017 18:02:11 UTC
All use subject to http://about.jstor.org/terms
475
Douglas H. Clements and Julie Sarama
concerns about a restricted category of educational technology, (c) a false
dichotomy of the goals of mathematics education, (d) a restricted view of teacher-
based instruction and computer interventions as necessarily distinct, and (e) a
restricted reporting of the research corpus.
First, Kitchen and Berk focus on hardware and software rather than the specific
content and pedagogy of computer interventions and the quality (e.g., fidelity) of
implementation of these facets within the ongoing curriculum. Years ago, Papert
(1985) called this “technocentrism”—a limited discussion of the technology, such
as “Does a computer (or computer-assisted instruction) do X or have effect Y?”
rather than a consideration of the content and teaching approach of particular
technologically based interventions. Software interventions differ markedly even
within categories, and aggregating them all via their delivery platform is an
analytical error that has been repeatedly identified (e.g., Clark, 1983; Clements &
Sarama, 2003; Tripp, 2001).
Second, and perhaps most misleading, Kitchen and Berk attend mainly to a
restricted category of educational technology but generalize their criticisms
beyond that category. They first define a term more broadly than usual (National
Mathematics Advisory Panel, 2008; Sarama & Clements, in press): “We use
‘computer-assisted instruction’ throughout this commentary to mean computer-
based interventions and computer-based training” (p. 4). However, they then
narrow this scope, defining computer-assisted instruction (CAI) as technology
that “provides self-paced instruction, tests, and learning feedback” (p. 5). The
problem, besides producing possible confusion, is that the authors then generalize
back to the universe of programs: “The concerns we express here are intended to
apply generally to any CAI intervention program designed for use in mathematics
classrooms in U.S. schools, and some of these concerns may apply for some CAI
programs and not for others” (p. 5). Not only do the authors claim that their
concerns apply to any CAI program, but the title of the piece does not refer to CAI
but to “educational technology,” which includes a range of pedagogical approaches,
types, and affordances much wider than those the authors mention briefly in a
footnote, such as multiple tools, online resources, interactive diagrams, computer
programming, exploratory environments, games, microworlds, different forms of
assessment, and, perhaps most important, combinations of these types with CAI
(Foster, Anthony, Clements, Sarama, & Williams, 2016; Hickey, Moore, &
Pellegrino, 2001; Sarama & Clements, in press). Given the article’s title and the
phrase “computer-based interventions” in the abstract, readers may understand-
ably believe that the authors’ concerns apply not just to a certain type of CAI but
also to the rich and variegated expanse of educational technology, which is not
necessarily true.
Further descriptions limit CAI even more to “drill and skill development”
(p. 9) software. In their conclusion, the authors state: “We question how well CAI
interventions can promote standards-based instruction in mathematics for under-
served students in which the development of students’ mathematical reasoning is
paramount” (p. 11). The authors raise a valid issue that CAI with a limited focus
This content downloaded from 128.138.154.222 on Wed, 01 Nov 2017 18:02:11 UTC
All use subject to http://about.jstor.org/terms
476 Response to Kitchen and Berk
on lower level skills and knowledge may be used too much with low-income
students and students of color, which is a long-standing concern (Becker, 2000;
Clements & Nastasi, 1992). However, this same concern applies to most curricular
components and teaching approaches (e.g., Bomer, Dworin, May, & Semingson,
2008; Darling-Hammond, 2007; Dyson, 2011; Ferguson, 2000; Lubienski, 2000).
Therefore, it is difficult to argue that educational technology is the cause, much
less the main cause, of a limited focus on lower level skills. Yet, that appears to
be the authors’ premise: “We discuss a challenge to realizing standards-based
reforms in mathematics in the United States: computer-based interventions in
mathematics classrooms” (p. 3). This again represents technocentric thinking, not
distinguishing between the many sociocultural and political contexts that lead to
particular purchases and uses of technology. To be clear, this does not diminish
the importance of recognizing the pernicious effects of overuse of low-level CAI
on low-income students and students of color, especially considering the marketing
of such programs. It is, however, equally important to point out that this is a ubiq-
uitous problem, not a problem with technology alone. Unless the systemic social–
cultural and political forces are confronted and altered, the effects of modifications
to technology alone will likely be trivial and temporary.
Third, to complicate matters further, some use of CAI for skill development
may not be contraindicated. Fluency is essential (Price, Mazzocco, & Ansari,
2013). In the survey results that the authors cite, 61% of teachers in low-poverty
schools vs. 83% of teachers in high-poverty schools report the use of technology
to learn or practice basic skills sometimes or often (Gray, Thomas, & Lewis, 2010).
This is the largest gap reported between these teachers, which is important but
hardly dichotomous. Further, the goals of “standards-based reform” depend on
the standards that one is discussing. The authors refer most often to the CCSSM,
which studiously avoid the false dichotomy—the often pernicious division
between mathematical skills and processes or between knowledge and reasoning.
From one perspective, then, the authors provide only a partial picture of those
standards—their emphasis on mathematical reasoning. As previously argued, they
also provide only a partial view of educational technology—CAI oriented toward
lower order skills. Subsequently, they express concern that CAI will not help
children achieve the standards by comparing the goals of only skills-oriented CAI
to the higher order aspects of CCSSM. Conclusions about “educational tech-
nology” from such comparisons will be misleading.
Fourth, as stated previously, the operational definition of CAI that the authors
use is “an instructional approach in which a computer, rather than an instructor,
provides self-paced instruction, tests, and learning feedback (Seo & Bryant,
2009)” (p. 5). We argue that this—computer versus instructor—is another false
dichotomy. The research includes numerous examples of computer interventions
in which interactions with teachers and computer software are combined to good
effect: whole-class instruction or sharing of work, collaborative work, and indi-
vidual work, to name a few. Such combinations can be more beneficial than either
approach used in isolation (e.g., Bakker, van den Heuvel-Panhuizen, & Robitzsch,
This content downloaded from 128.138.154.222 on Wed, 01 Nov 2017 18:02:11 UTC
All use subject to http://about.jstor.org/terms
477
Douglas H. Clements and Julie Sarama
2015; Clements & Sarama, 1997, 2007; Clements, Sarama, Spitler, Lange, & Wolfe,
2011; Naidoo & Naidoo, 2007; Primavera, Wiederlight, & DiGiacomo, 2001).
Fifth, the authors report only selected portions of the research corpus. For
example, they express “wonder” or “concern” (pp. 5, 9, etc.) about the role of CAI
when there are available studies to address the issues raised. For example, different
types of educational technology—CAI being only one—can benefit students of
all ages in a range of mathematics competencies, including reasoning and problem
solving (e.g., Bakker et al., 2015; Bayturan & Kesan, 2012; Choi-koh, 1999; Craig
et al., 2011; Kong, 2011; McCoy, 1991; National Mathematics Advisory Panel,
2008; Rakes, Valentine, McGatha, & Ronau, 2010; Schacter & Jo, 2016; Sheard
& Chambers, 2011; Shin, Sutherland, Norris, & Soloway, 2012; Thompson &
Davis, 2014; Tucker, 2015). Evidence also indicates that no piece of software,
regardless of type, used in any way will result in gains (Dynarski et al., 2007;
National Mathematics Advisory Panel, 2008); overall, only small or moderate
effects can presently be expected. For example, a meta-analysis of supplemental
CAI, computer-management learning, and comprehensive programs reported an
effect size of +.15 standard deviations (Cheung & Slavin, 2013). Harskamp (2014)
reports larger effect sizes, +.48 standard deviations for CAI and exploratory envi-
ronments. Given that gains are moderate, we agree with Kitchen and Berk that
educators should investigate whether “decisions to purchase are based on politics,
personal preferences, and marketing; it is more a matter of slick public relations
pitches rather than the effectiveness of the actual products that influence sales”
(p. 9). However, this unfortunate situation may apply equally to curricula, profes-
sional development, and charter schools. Notably, CAI has been found to be more
cost-effective than conventional instruction (Fletcher, Hawley, & Piele, 1990) and
other interventions, such as peer tutoring and reducing class size (Niemiec &
Walberg, 1987). However, in the context of Kitchen and Berk’s argument, concerns
about funding are directed only to CAI.
Further, these concerns are sometimes directly contradicted by research on the
use of CAI with low-income students and students of color as well as students with
disabilities. For example, there is a substantial history of research showing and
documenting the efficacy of multiple ways that educational technology can be
used to address equity concerns (Bakker et al., 2015; Brown & Boshamer, 2000;
Campbell, 1985; Day, 2002; Fletcher-Flinn & Gravatt, 1995; Harskamp, 2014;
Hasselbring, 1986; Hativa, 1994; Helsel, Hitchcock, Miller, Malinow, & Murray,
2006; Hotard & Cortez, 1983; Judge, 2005; Li, Atkins, & Stanton, 2006;
McConnell, 1983; Owens & Waxman, 1994; Ragosta, Holland, & Jamison, 1982;
Sarama & Clements, in press; Slavin & Lake, 2008). Similar research supports
approaches other than, or blended with, CAI (e.g., Clements & Sarama, 1997;
Corning & Halapin, 1989; Edyburn, 2000; Heid, 1997; Hickey et al., 2001; Hughes,
1986; Weir, 1987; Zucker & Light, 2009). Our own work uses software suites that
combine software types, such as instructionally oriented CAI with collaborative
problem-solving tools, to help children progress along learning trajectories. This
approach has worked well both when integrated with teacher-initiated instruction,
This content downloaded from 128.138.154.222 on Wed, 01 Nov 2017 18:02:11 UTC
All use subject to http://about.jstor.org/terms
478 Response to Kitchen and Berk
showing significant mediation of child gain (Clements & Sarama, 2007; Clements
et al., 2011), and when used separately (Foster et al., 2016). These studies are not
cited by Kitchen and Berk, who state that there is a need for research on such
approaches in “schools with large numbers of underserved students” (p. 9).
Furthermore, these studies’ positive evidence was gathered in many such schools,
and they used learning trajectories to support one of the most effective of educa-
tional strategies, formative assessment (National Mathematics Advisory Panel,
2008; Penuel & Shepard, 2016), including individualized instruction, which the
CAI supported. Kitchen and Berk agree with CAI’s strengths in this limited area
but state that “this strength could also become a weakness if students consistently
engage in mathematics alone” (p. 9; we assume that they mean individual, skill-
based instruction). In response, we repeat our earlier point that successful
approaches often use CAI and other types of educational technology that blend a
variety of pedagogical approaches, including collaborative work. Interestingly,
across educational technology environments, even CAI, children prefer collab-
orative use and interact with each other, talking about computer tasks even when
using “individual” CAI, as much or more in technological environments as they
do in nontechnological environments (Clements & Nastasi, 1992; Klinzing & Hall,
1985). As a final example, some studies have shown that educational technology
is more effective in promoting “discourse in which mathematical ideas are gener-
ated, shared, investigated, debated, and validated” (Kitchen & Berk, 2016, p. 11)
than are other instructional strategies (Clements & Sarama, 2003).
Finally, Kitchen and Berk’s rhetorical framing of the research may mislead some
readers. The advantages of CAI are described with studies of “a CAI program” or
“many types of programs,” but the weaknesses are introduced in a statement that
appears to militate against the use of any CAI: “In terms of weaknesses, studies
have also shown that use of CAI does not affect student achievement in mathe-
matics” (p. 8).
In conclusion, the general issues that Kitchen and Berk raise are important,
although such concerns have been raised over the last 30 years (e.g., Becker, 2000;
Gay, 1989; Heid, 1997; Weir, 1987; Wenglinsky, 1998). The use of drill alone is
ill-advised and even ineffective for limited goals of memorization or fluency (e.g.,
Clements & Nastasi, 1985, 1993; Haugland, 1992; Henry & Brown, 2008). To
promote equity, we need to provide teachers in lower income schools with time,
practice, and support to develop methods for using effective approaches, including
challenging software (Becker, 2000; Clements & Sarama, 2003). However, the
field needs to move past simply stating established concerns and use all knowledge
from research and practice to establish guidelines and models for educational
technology—including the overarching and specific contexts in which it may be
useful and cost-efficient and those contexts in which it is not. We need continued
research on new forms and new applications of educational technology. We need
more research on how increasing the use of educational technology may be exac-
erbating equity problems and what can be done to reverse that trend. Finally,
discussions of educational technology need to distinguish technocentric
This content downloaded from 128.138.154.222 on Wed, 01 Nov 2017 18:02:11 UTC
All use subject to http://about.jstor.org/terms
479
Douglas H. Clements and Julie Sarama
perspectives from broader studies of policy and extract the lessons learned from
the full research corpus to direct future research, policy, and practice involving
educational technology. Overly limited and overgeneralized arguments do not
serve those ends.
References
Bakker, M., van den Heuvel-Pan huizen, M., & Robitzsch, A. (2015). Effects of playing mathematics
computer games on primary school students’ multiplicative reasoning ability. Contemporary
Educational Psychology, 40, 55–71. doi:10.1016/j.cedpsych.2014.09.001
Bayturan, S., & Kesan, C. (2012). The effect of computer-assisted instruction on the achievement
and attitudes towards mathematics of students in mathematics education. International Journal
of Global Education, 1(2), 5 0 –57.
Becker, H. J. (2000). Who’s wired and who’s not: Children’s access to and use of computer
technology. The Future of Children, 10 (2), 44 –75. doi:10.2307/1602689
Bomer, R., Dworin, J. E., May, L., & Semingson, P. (2008). Miseducating teachers about the poor:
A critical analysis of Ruby Payne’s claims about pover ty. Teachers College Record, 110(12),
2497–2531.
Brown, F., & Boshamer, C. C. (2000). Computer assisted instruction in mathematics can improve
students’ test scores: A study. Retrieved from ERIC database. (ED443688)
Campbell, P. B. (1985, April). Hidden equity: Incorporating equity in existing computer-based
programs. Paper presented at the annual meeting of the American Educational Research
Association, Chicago, IL.
Cheung, A. C. K., & Slavin, R. E. (2013). The effectiveness of educational technology applications
for enhancing mathematics achievement in K-12 classrooms: A meta-analysis. Educational
Research Review, 9, 88–113. doi:10.1016/j.ed urev.2013.01.001
Choi-koh, S. S. (1999). A student’s learning of geometry using the computer. The Journal of
Educational Research, 92(5), 301–311. doi:10.1080/00220679909597611
Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational
Research, 53(4), 445–459. doi:10.3102/00346543053004445
Clements, D., & Nastasi, B. (1985). Effects of computer environments on social-emotional
development: Logo and computer-assisted instr uction. Computers in the Schools, 2(2–3), 11–31.
doi:10.1300/J025v02n02_04
Clements, D. H., & Nastasi, B. K. (1992). Computer s and early childhood educat ion. In M. Gettinger,
S. N. Elliott, & T. R. Kratochwill (Eds.), Preschool and early childhood treatment directions (pp.
187–246). Hillsdale, NJ: Erlbaum.
Clements, D. H., & Nastasi, B. K. (1993). Electronic media and early childhood education. In B.
Spodek (Ed.), Handbook of research on the educat ion of young children (pp. 251–275). New
York, NY: Macmillan.
Clements, D. H., & Sarama, J. (1997). Research on Logo: A decade of progress. Computers in the
Schools, 14(1–2), 9–46. doi:10.1300/J025v14n01_02
Clements, D. H., & Sarama, J. (2003). Strip mining for gold: Research and policy in educational
technology—A response to “Fool’s Gold.” Educational Technology Review, 11(1) , 7– 69.
Clements, D. H., & Sarama, J. (2007). Effects of a preschool mathematics curriculum: Summative
research on the Building Blocks project. Journal for Research in Mathematics Education, 38(2),
136–163.
Clements, D. H., Sarama, J., Spitler, M. E., Lange, A. A., & Wolfe, C. B. (2011). Mathematics
learned by young children in an intervention based on learning trajectories: A large-scale cluster
randomized trial. Journal for Research in Mathematics Education, 42(2), 127–16 6.
Corning, N., & Halapin, J. (1989, March). Comp uter applications in an action-or iented
kindergarten. Paper presented at the meeting of the Connecticut Institute for Teaching and
Learning Conference, Wallingford, CT.
This content downloaded from 128.138.154.222 on Wed, 01 Nov 2017 18:02:11 UTC
All use subject to http://about.jstor.org/terms
480 Response to Kitchen and Berk
Craig, S. D., Anderson, C., Bargagloitti, A., Graesser, A. C., Okwumabua, T., Sterbinsky, A., & Hu,
X. (2011). Learning with ALEKS: The impact of students’ attendance in a mathematics after-
school program. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Articial intelligence in
education (pp. 435–437). Berlin, Germany: Springer.
Darling-Hammond, L. (2007). The at earth and education: How America’s commitment to equity will
determine our future. Educational Researcher, 36(6) , 318–33 4. doi :10.3102 /0013189X07308253
Day, S. L. (2002). Real kids, real risks: Effective instruction of students at risk of failure. NASS P
Bulletin, 86(632), 19–32. doi:10.1177/019263650208663203
Dynarski, M., Agodini, R., Heaviside, S., Novak, T., Carey, N., Campuzano, L., . . . Sussex, W.
(2007). Effectiveness of reading and mathematics software products: Findings from the rst
student cohort. Washington, DC: U.S. Department of Education, I nstitute of Education Sciences.
Dyson, N. I. (2011). A number sense intervention for urban kindergartners at risk for mathematics
difculties (Doctoral dissertation). Retrieved from UMI ProQuest database. (UMI No. 3465744)
Edyburn, D. L. (2000). Assistive technology and students with mild disabilities. Focus on
Exceptional Children, 32(9), 1–24.
Ferguson, A. A. (2000). Bad boys: Public schools in the making of black masculinity. Ann Arbor,
MI: University of Michigan Press. doi:10.3998/mpub.16801
Fletcher, J. D., Hawley, D. E., & Piele, P. K. (1990). Costs, effects, and utility of microcomputer
assisted instruction in the classroom. American Educational Research Journal, 27(4), 783–806.
doi:10.3102/00028312027004783
Fletcher-Flinn, C. M., & Gravatt, B. (1995). The efcacy of computer assisted instr uction (CAI): A
meta-analysis. Journal of Educational Computing Research, 12(3), 219 241. doi:10. 2190/51D4 -
F6L3-JQHU-9M31
Foster, M. E., Anthony, J. L., Clements, D. H., Sarama, J., & Williams, J. M. (2016). Improving
mathematics learning of kindergarten students through computer-assisted instruction. Journal
for Research in Mathematics Education, 47(3), 206–232. doi:10.5951/jresematheduc.47.3.0206
Gay, P. (1989). Tactile turtle: Explorations in space with visually impaired children and a oor
turtle. British Journal of Visual Impairment, 7(1), 23–25. doi:10.1177/026461968900700106
Gray, L., Thomas, N., & Lewis, L. (2010). Teachers’ use of educational technology in U.S. public
schools: 2009 (NCES 2010-040). Washington, DC: U.S. Department of Education, Institute of
Education Sciences, National Center for Education Statistics.
Harskamp, E. (2014). The effects of computer technology on primary school students’ mathematics
achievement: A meta-analysis. In S. Chinn (Ed.), The Routledge international handbook of
dyscalculia and mathematical learning difculties (pp. 383–392). London, UK: Routledge.
Hasselbring, T. S. (1986). Research on the effectiveness of computer-based instruction: A review.
International Review of Education, 32(3), 313–324. doi:10.1007/BF02426065
Hativa, N. (1994). Cognitive and affective effects of computer-based arithmetic practice on the
lowest achieving students. In J. E. H. van Luit (Ed.), Research on learning and instruction of
mathematics in k indergarten and primar y school (pp. 303–327). Doetinchem, the Netherlands:
Graviant.
Haugland, S. W. (1992). The effects of computer software on preschool children’s developmental
gains. Journal of Computing in Childhood Education, 3(1), 15–30.
Heid, M. K. (1997). The technological revolution and the reform of school mathematics. American
Journal of Education, 106 (1), 5– 61. doi:10.1086/444175
Helsel, F. K. I., Hitchcock, J. H., Miller, G., Malinow, A., & Murray, E. (2006, April). Identifying
evidence-ba sed, promising and emerging practices that use screen-based and calculator
technology to teach mathematics in g rades K-12: A research synthesis. Paper presented at the
annual meeting of the American Educational Research Association, San Francisco, CA.
Henry, V. J., & Brown, R. S. (2008). First-grade basic facts: An investigation into teaching and
learning of an accelerated, high-demand memorization standard. Journal for Research in
Mathematics Education, 39(2), 153–183.
Hickey, D. T., Moore, A. L., & Pellegrino, J. W. (2001). The motivational and academic
consequences of elementary mathematics environments: Do constructivist innovations
This content downloaded from 128.138.154.222 on Wed, 01 Nov 2017 18:02:11 UTC
All use subject to http://about.jstor.org/terms
481
Douglas H. Clements and Julie Sarama
and reforms make a difference? American Educational Research Journal, 38(3), 611–652.
doi:10.3102/00028312038003611
Hotard, S. R., & Cortez, M. J. (1983). Computer-assisted instr uction as an enhancer of remediation.
Lafayette Parish, LA: Lafayette Parish.
Hughes, M. (1986). Children and number: Difculties in learning mathematics. Oxford, UK:
Blackwell.
Judge, S. (2005). The impact of computer technology on academic achievement of young
African American children. Journal of Research in Childhood Educat ion, 20(2), 91–101.
doi:10.1080/02568540509594554
Kitchen, R., & Berk, S. (2016). Educational technology: An equity challenge to the Common Core.
Journa l for Research in Ma thematic s Educatio n, 47(1), 3–16. doi:10.5951/jresemathe duc.47.1.0003
Klin zing, D. G., & Hall, A. (1985, April). A study of the behavior of children in a preschool equipped
with computers. Paper presented at the annual meeting of the American Educational Research
Association, Chicago, IL.
Kong, S. C. (2011). An evaluation study of the use of a cognitive tool in a one-to-one classroom
for promoting classroom-based dialogic interaction. Computers & Education, 57(3), 1851–18 64.
doi:10.1016/j.compedu.2011.04.008
Li, X., Atkins, M. S., & Stanton, B. (2006). Effects of home and school computer use on school
readiness and cognitive development among Head Start children: A randomized controlled pilot
trial. Merrill-Palmer Quarterly, 52(2), 239–263. doi:10.1353/mpq.2006.0010
Lubienski, S. T. (2000). Problem solving as a means toward mathematics for all: An exploratory
look through a class lens. Journal for Research in Mathematics Education, 31(4), 454– 482.
doi:10.2307/749653
McConnell, B. B. (1983). Evaluation of computer inst ruction in math (nal report). Pasco, WA:
Pasco School District.
McCoy, L. P. (1991). The effect of geometr y tool software on high school geometry achievement.
Journal of Computers in Mathematics and Science Teaching, 10 (3) , 51–57.
Naidoo, N., & Naidoo, R. (2007). Using blended learning to facilitate the mathematical thought
processes of primary school learners in a computer laboratory: A case study in calculating simple
areas. Jour nal of College Teaching & Learning, 4(7), 79–94.
National Governors Associat ion Center for Best Practices & Council of Chief State School Ofcers.
(2010). Common Core State Standards for Mathematics. Washington, DC: Authors.
National Mathematics Advisory Panel. (2008). Foundations for success: The nal report of the
National Mathematics Advisory Panel. Washington DC: U.S. Department of Education.
Niemiec, R., & Walberg, H. J. (1987). Comparative effects of computer-assisted instruction: A
synthesis of reviews. Journal of Educational Computing Research, 3(1), 19–37. doi:10.2190/
RMX5-1LTB-QDCC-D5HA
Owens, E. W., & Waxman, H. C. (1994). Comparing the effectiveness of computer-assisted
instruction and conventional instruction in mathematics for African-American postsecondary
students. International Journal of Instructional Media, 21(4), 327–336.
Papert, S. (1985). Computer criticism vs. technocentric thinking. In Logo 85 Theoret ical Papers
(pp. 53–67). Cambridge, MA: Massachusetts Institute of Technology.
Penuel, W. R., & Shepard, L. A. (2016). Assessment and teaching. In D. H. Gitomer & C. A. Bell
(E ds.), Handbook of research on teaching (5th ed., pp. 787–850). Washington, DC: American
Educational Research Association. doi:10.3102/978-0-935302-48-6_12
Price, G. R., Mazzocco, M. M. M., & Ansari, D. (2013). Why mental arithmetic counts: Brain
activation during single digit arithmetic predicts high school math scores. The Journal of
Neuroscience, 31(1), 156–163. doi:10.1523/JNEUROSCI.2936-12.2013
Primavera, J., Wiederlight, P. P., & DiGiacomo, T. M. (2001, August). Technology access for low-
income preschoolers: Bridging the digital divide. Paper presented at the annual meeting of the
American Psychological Association, San Francisco, CA.
Ragosta, M., Holland, P. W., & Jamison, D. T. (1982). Computer-assisted in struction and
compensatory education: T he ETS/LAUSD study. T he nal report. Princeton, NJ: Educational
Testing Service.
This content downloaded from 128.138.154.222 on Wed, 01 Nov 2017 18:02:11 UTC
All use subject to http://about.jstor.org/terms
482 Response to Kitchen and Berk
Rakes, C. R., Valentine, J. C., McGatha, M. B., & Ronau, R. N. (2010). Methods of instructional
improvement in algebra: A systematic review and meta-analysis. Review of Educational
Research, 80(3), 37240 0. doi:10.3102/0034654310374880
Sarama, J., & Clements, D. H. (in press). Promoting a good start: Technology in early childhood
mathematics. In E. Arias, J. Cristia, & S. Cueto (Eds.), Promising models to improve primary
mathematics learning in Latin America and the Caribbean using technolog y. Washington, DC:
Inter-American Development Bank.
Schacter, J., & Jo, B. (2016). Improving low-income preschoolers mathematics achievement with
Math Shelf, a preschool tablet computer curriculum. Computers in Human Behavior, 55(A),
223–229. doi:10.1016/j.chb.2015.09.013
Sheard, M., & Chambers, B. (2011). Self-paced learning: Effective technology-supported formative
assessment. York, UK: University of York, Institute for Effective Education.
Shin, N., Sutherland, L. M., Norris, C. A., & Soloway, E. (2012). Effects of game technology on
elementary student lear ning in mathematics. British Journal of Educational Technology, 43(4),
54 0–56 0. d oi:10.1111/j.1467- 8535.2 011.01197.x
Slavin, R. E., & Lake, C. (2008). Effective programs in elementary mathematics: A best-evidence
synthesis. Review of Educational Research, 78(3), 427–515. doi:10.3102/0034654308317473
Thompson, C. J., & Davis, S. B. (2014). Classroom observation data and instruction in primary
mathematics education: Improving desig n and rigour. Mathematics Education Research Journal,
26(2), 301–323. doi:10.10 07/s13394- 013-0 099-y
Tripp, S. D. (2001). Media and learning. Retrieved April 7, 2001, from http://itech1.coe.uga.edu/
itforum/p ape r16/pap er16. html
Tucker, S. I. (2015). An exploratory st udy of attributes, affordances, abilities, and distance in
students’ use of mathematics virtual manipulat ive iPad apps (Doctoral dissertation). Utah State
University, Logan, UT.
Weir, S. (1987). Cultivating minds: A Logo casebook. New York, NY: Harper & Row.
Wenglinsky, H. (1998). Does it compute? The relationship bet ween educational technology and
student achievement in mathematics. Princeton, NJ: Educational Testing Ser vice. Retrieved f rom
https://www.ets.org/Media/Research/pdf/PICTECHNOLOG.pdf
Zucker, A. A., & Light, D. (2009). Laptop programs for students. Science, 323(5910), 82–85.
do i:10.112 6/s cie n ce.116770 5
Authors
Douglas H. Clements, Morgridge College of Education, Marsico and Kennedy Institutes,
University of Denver, Katherine A. Ruffatto Hall 154, 1999 East Evans Avenue, Denver, CO
80208-1700; Douglas.Clements@du.edu
Julie Sarama, Morgridge College of Education, Marsico and Kennedy Institutes, University of
Denver, Kathe rine A. Ruffatto Hall 154, 1999 East Evans Avenue, Denver, CO 80208-1700; Julie.
Sarama@du.edu
Submitted February 20, 2016
Accepted July 19, 2016
This content downloaded from 128.138.154.222 on Wed, 01 Nov 2017 18:02:11 UTC
All use subject to http://about.jstor.org/terms
... Researchers have emphasized that technologies are neither inherently good nor are they bad, but rather that the pedagogical uses of technological advances must be considered to truly highlight their promises and impacts (Clements & Sarama, 2017). Educational technology can both scaffold students' engagement in STEM practices and contribute to equitable and inclusive learning. ...
... In overcoming the LMS limitation of failing to maintain interactive or practical learning, the findings have proven that SMS like Facebook, WhatsApp, Pinterest, YouTube, wikis and others have the potential to maintain participatory and active learning; hence simulation videos can be shared and discussed. Similarly, Clements and Sarama (2017) further assert that the use of technologies like computer-assisted instruction also enhance informal learning in science, for students to master formulas, equations, and theories. Fomunyam (2019) argues that SMS use inside and outside the classroom is the best way to acquire knowledge. ...
Article
Full-text available
By being oblivious to the recent paradigm shift from formal learning to informal learning platforms, higher education institutions (HEIs) disadvantage student learning in the digital age. With the aim of bringing awareness of the need to shift from the use of learning management systems (LMS) to social media sites (SMS), this study explores students’ experiences of the use of SMS for learning science modules. This qualitative interpretive case study was carried out at two universities, with electronic reflective activities, Zoom focus group interviews and WhatsApp one-on-one semi-structured interviews used to generate data. The sample was a total of 47 students purposively selected from science modules in a teacher education programme at two schools of education, one in South Africa and one in the United States of America. Data were thematically analysed and framed by social constructivism and connectivism. Findings indicated that learning of science modules is mainly through LMS, at the expense of SMS which are preferred by the students. The study concludes that since SMS are used effectively for students’ communication and collaboration outside of the lecture hall, then HEIs need to shift to thinking about bringing these SMS inside and putting them to use for effective learning.
... El 12% de los artículos restantes no especifican o no se centran en una etapa educativa concreta o en algunos casos integran distintas etapas educativas. Como los trabajos de Baglama et al (2018) que realizan un análisis bibliográfico sobre el uso de TIC con niños sordos; Clements & Sarama (2017) publicaron una reseña para la mejora de las matemáticas entre el alumnado con pocos recursos, Dolan (2016) que realiza un análisis sobre la brecha digital, Emiroğlu & Kurt (2017) publicaron un trabajo a través de una aplicación virtual, Cole, Cohen, Wilhelm & Lindell, (2018) realizaron una investigación para el desarrollo de la inteligencia espacial en el marco de la astronomía, el trabajo de Azigwe et al., (2016) consistió en encuestar al alumnado de escuelas de Ghana acerca de la calidad de la enseñanza de las Matemáticas por parte de sus profesores, Barzillai & Thomson (2017) que dedicaron un capítulo de un libro al uso de ebooks para la alfabetización de niños. Otro 6%, corresponde a encuestas y proyectos que tienen al profesorado como protagonista al implantar o cubrir las aulas con recursos TIC. ...
Article
Full-text available
Resumen La incorporación de las tabletas en muchas escuelas a lo largo del mundo, ha despertado la curio-sidad de la comunidad científica, justificando el auge de reseñas, noticias, publicaciones derivadas de la investigación, la celebración de congresos y exposiciones o la proliferación de libros. En este contexto surge la necesidad de conocer y analizar aquello que sea de interés. El presente artículo re-coge los resultados de una revisión bibliográfica a partir de las publicaciones localizadas en la base de datos de Scopus, del periodo del 2016 al 2019. El objetivo principal fue identificar y analizar las 50 publicaciones seleccionadas como muestra, recogidas de revistas, libros y libros de actas en la base de datos Scopus cuya temática abordase la investigación y uso de la Tablet en la escuela en los últimos cuatro años. En lo metodológico se ha optado por el análisis documental, que permite extraer datos de índole cuantitativa y cualitativa. Los resultados reflejan la importante presencia de la Tablet en las escuelas, aunque se sigue perci-biendo una gran brecha digital entre países. En general, la mayoría de las investigaciones reflejan un impacto positivo en los sistemas educativos analizados, repercutiendo de manera eficiente en la motivación del alumnado y su adquisición de competencias. Sin embargo, para que el resultado de su uso sea exitoso es necesaria la introducción de metodologías pedagógicas que favorezcan el apren-dizaje interdisciplinar y cooperativo.
... La etapa educativa más representada es la Educación Elemental o Primaria que asciende a un 60% de los trabajos analizados. Las temáticas abordadas en todas las etapas son muy diversas, pero abundan las que investigan la aplicación de las herramientas TIC en al aula (Ale, Loh & Chib,2017;Al-Mashaqbeh, 2016;Altan & Karalar, 2018;Avidov-Ungar & Shamir-Inbal, 2017), en general o en las distintas áreas de conocimiento (Chen, Chiu, Lin & Chou, 2017;Dahan, Barzillai & Katzir, 2018;Falloon, 2017), en muchas ocasiones, con el objetivo de responder a la atención a la diversidad dentro del aula (Fleisch, Schoer, Roberts, Thornton, 2016;Fletcher-Watson, Pain, Hammond, Humphry, McConachie, 2016) (2018) que realizan un análisis bibliográfico sobre el uso de TIC con niños sordos; Clements & Sarama (2017) publicaron una reseña para la mejora de las matemáticas entre el alumnado con pocos recursos, Dolan (2016) que realiza un análisis sobre la brecha digital, Emiroğlu & Kurt (2017) publicaron un trabajo a través de una aplicación virtual, Cole, Cohen, Wilhelm & Lindell, (2018) realizaron una investigación para el desarrollo de la inteligencia espacial en el marco de la astronomía, el trabajo de Azigwe et al., (2016) consistió en encuestar al alumnado de escuelas de Ghana acerca de la calidad de la enseñanza de las Matemáticas por parte de sus profesores, Barzillai & Thomson (2017) que dedicaron un capítulo de un libro al uso de ebooks para la alfabetización de niños. Otro 6%, corresponde a encuestas y proyectos que tienen al profesorado como protagonista al implantar o cubrir las aulas con recursos TIC. ...
Article
Full-text available
... El 12% de los artículos restantes no especifican o no se centran en una etapa educativa concreta o en algunos casos integran distintas etapas educativas. Como los trabajos de Baglama et al (2018) que realizan un análisis bibliográfico sobre el uso de TIC con niños sordos; Clements & Sarama (2017) publicaron una reseña para la mejora de las matemáticas entre el alumnado con pocos recursos, Dolan (2016) que realiza un análisis sobre la brecha digital, Emiroğlu & Kurt (2017) publicaron un trabajo a través de una aplicación virtual, Cole, Cohen, Wilhelm & Lindell, (2018) realizaron una investigación para el desarrollo de la inteligencia espacial en el marco de la astronomía, el trabajo de Azigwe et al., (2016) consistió en encuestar al alumnado de escuelas de Ghana acerca de la calidad de la enseñanza de las Matemáticas por parte de sus profesores, Barzillai & Thomson (2017) que dedicaron un capítulo de un libro al uso de ebooks para la alfabetización de niños. Otro 6%, corresponde a encuestas y proyectos que tienen al profesorado como protagonista al implantar o cubrir las aulas con recursos TIC. ...
Article
Full-text available
La incorporación de las tabletas en muchas escuelas a lo largo del mundo, ha despertado la curiosidad de la comunidad científica, justificando el auge de reseñas, noticias, publicaciones derivadas de la investigación, la celebración de congresos y exposiciones o la proliferación de libros. En este contexto surge la necesidad de conocer y analizar aquello que sea de interés. El presente artículo recoge los resultados de una revisión bibliográfica a partir de las publicaciones localizadas en la base de datos de Scopus, del periodo del 2016 al 2019. El objetivo principal fue identificar y analizar las 50 publicaciones seleccionadas como muestra, recogidas de revistas, libros y libros de actas en la base de datos Scopus cuya temática abordase la investigación y uso de la Tablet en la escuela en los últimos cuatro años. En lo metodológico se ha optado por el análisis documental, que permite extraer datos de índole cuantitativa y cualitativa.
Article
In our response to Clements and Sarama (2017), we address the 5 issues that they identify as criticisms of our Research Commentary (Kitchen & Berk, 2016). As in our original commentary, we highlight concerns we have regarding the delivery of CAI programs and potential misuses of CAI, particularly at Tide I schools that largely serve historically marginalized student groups. Specifically, we concentrate on how CAI may contribute to underserved students generally experiencing mathematics in impoverished ways that do not align with reforms being advocated by the mathematics education community. We also argue that Clements and Sarama appear to dismiss or ignore our central argument that some CAI programs are not designed or are not being used to support the development of students' mathematical reasoning and fluency.
Book
Full-text available
Congress posed questions about the effectiveness of educational technology and how effectiveness is related to conditions and practices. The study identified reading and mathematics software products based on prior evidence of effectiveness and other criteria and recruited districts, schools, and teachers to implement the products. On average, after one year, products did not increase or decrease test scores by amounts that were statistically different from zero. For first and fourth grade reading products, the study found several school and classroom characteristics that were correlated with effectiveness, including student-teacher ratios (for first grade) and the amount of time products were used (for fourth grade). The study did not find characteristics related to effectiveness for sixth grade math or algebra. The study also found that products caused teachers to be less likely to lecture and more likely to facilitate, while students using reading or mathematics software products were more likely to be working on their own. The results reported here are based on schools and teachers who were not using the products in the previous school year. Whether products are more effective when teachers have more experience using them is being examined with a second year of data. The study will involve teachers who were in the first data collection (those who are teaching in the same school and at the same grade level or subject area) and a new group of students. The second-year study will also report results separately for the various products.
Article
Full-text available
This study evaluated the effects of a mathematics software program, the Building Blocks software suite, on young children's mathematics performance. Participants included 247 Kindergartners from 37 classrooms in 9 schools located in low-income communities. Children within classrooms were randomly assigned to receive 21 weeks of computer-assisted instruction (CAI) in mathematics with Building Blocks or in literacy with Earobics Step 1. Children in the Building Blocks condition evidenced higher posttest scores on tests of numeracy and Applied Problems after controlling for beginning-of-year numeracy scores and classroom nesting. These findings, together with a review of earlier CAI, provide guidance for future work on CAI aiming to improve mathematics performance of children from low-income backgrounds.
Thesis
Full-text available
This exploratory qualitative study investigated the presence of and relationships among constructs that contribute to children’s interactions with educational technology, leading to the development of the Modification of Attributes, Affordances, Abilities, and Distance (MAAAD) for Learning framework. For this study, each of 10 fifth-grade children participated in one individual video-recorded semistructured interview session, during which they interacted with two mathematics virtual manipulative iPad apps and responded to follow-up questions. Video recordings and observation field notes were analyzed for evidence of attributes, affordance-ability relationships, distance, and relationships among these constructs. Constant comparative data analysis using memoing and eclectic coding provided evidence of the presence of each focus construct. Further analysis and interpretation, including quantization of qualitative data for visualization using novel rhombus plots, also led to the identification of emergent themes related to each construct and revealed relationships among the constructs. Emergent themes included categorization, alignment, and modification of attributes, variations and interrelationships among affordance-ability relationships, and the identification of and interactions among mathematical and technological distance. Furthermore, each construct related to each other construct. The evidence and interpretations led to the development of the MAAAD for Learning framework. The results of the study suggest that the MAAAD for Learning framework models relationships among attributes, affordance-ability relationships, and distance in the context of user-app interactions. The framework could serve as a tool for app developers designing apps, educators using apps to support children’s learning, and researchers characterizing user-app interactions and the outcomes of those interactions. The constructs, relationships, and framework identified in this study advance the literature on children’s interactions with educational technology tools, in particular literature concerning children’s interactions with mathematics virtual manipulative iPad apps.
Article
The implementation of the Common Core State Standards for Mathematics (National Governors Association Center for Best Practices & Council of Chief State School Officers, 2010) has the potential to move forward key features of standards-based reforms in mathematics that have been promoted in the United States for more than 2 decades (e.g., National Council of Teachers of Mathematics, 1989,2000; National Science Foundation, 1996). We believe that this is an especially opportune time to purposely focus on improving the mathematics education of students who have historically been denied access to a high-quality and rigorous mathematics education in the United States, specifically low-income students and students of color (e.g., Kitchen, DePree, Celedon-Pattichis, & Brinkerhoff, 2007; Leonard & Martin, 2013). We discuss a challenge to realizing standards-based reforms in mathematics in the United States: computer-based interventions in mathematics classrooms.
Article
Background/Context This is the first research study to examine the content basis of Payne's in-service teacher education program, A Framework for Understanding Poverty, though others who have reviewed the book have agreed with our analysis. The study took place within a policy context in which the federal government, with the passage of the No Child Left Behind Act (2002), created a new category of students (economically disadvantaged) whose test scores would be monitored by officials in the U. S. Department of Education. This law ensures that the improvement of poor children's test scores becomes a major concern of every public school in the country. These federal requirements have fueled the demand for professional development programs such as that offered by Ruby Payne and her Aha! Process, Inc. Purpose This article reports on an examination of the content of Ruby Payne's professional development offerings, as represented in A Framework for Understanding Poverty. Given the immense popularity of the program, an assessment of its representations of poor people is warranted and significant. We analyzed the relationship between Payne's claims and the existing research about low-income individuals and families. This study of Payne's work provides administrators and teachers with an evaluation of the reliability of Payne's claims. It also provides scholars in education, anthropology, sociology, and related fields with a description and critique of one of the more common conversations that is engaging teachers about the nature of the lives of many of their students, and the struggle to identify directions in which to improve schooling for the most vulnerable students in the education system. Research Design This is a qualitative research study whose data were derived from an analysis of A Framework for Understanding Poverty. Conclusions/Recommendations Our critical analysis of Payne's characterizations of people living in poverty indicates that her work represents a classic example of what has been identified as deficit thinking. We found that her truth claims, offered without any supporting evidence, are contradicted by anthropological, sociological and other research on poverty. We have demonstrated through our analysis that teachers may be misinformed by Payne's claims. As a consequence of low teacher expectations, poor students are more likely to be in lower tracks or lower ability groups and their educational experience more often dominated by rote drill and practice.
Article
Low-income preschoolers begin Kindergarten behind their middle and high-income peers in mathematics, and these achievement differences grow as they progress through school. Technology can provide cost effective and scalable solutions to improve young children's mathematics outcomes (Levin, Glass, & Meister, 1987; Slavin & Lake, 2008). The aim of this study was to test Math Shelf, a tablet computer curriculum designed to improve at risk preschoolers' mathematics performance. Two hundred and seventy-three children participated with intervention students playing Math Shelf on tablets for 15 weeks, while comparison students participated in their regular classroom mathematics curriculum. At the end of the intervention, there was a significant and sizable effect on the mathematics posttest for Math Shelf students (Cohen's d = 1.09, p <.001). Math Shelf students learned approximately one year more mathematics than control students. Our results suggest that teachers can significantly increase low-income preschoolers' mathematics knowledge in a relatively short amount of time by implementing evidenced-based tablet software.