Jeff Burgess

Action Research Project:
Using Technology to Improve Mathematics Instruction

EAD 6062/6072
Elementary Administration Internship

presented to
Dr. Michael Arnold
Southwest Baptist University

Fall 2003 – Spring 2004

Chapter 1:  Using Technology to Improve Learning

Introduction
	Many school mission statements include comments related to producing capable, informed, productive citizens that can function effectively in today’s society.   Is it possible to be effective in our twenty-first century world without a variety of computer and technical skills?  Can these same skills necessary for humanity be introduced as an agent for improving instruction in the elementary classroom?

Problem Statement
Two concerns listed for the Greenfield R-IV School District’s Comprehensive School Improvement Plan are general achievement scores (beginning with grade four) and teacher and student training in technology.  It is probable that some students view academic achievement as irrelevant and meaningless.  

Definition of Terms
	Accessibility – the level of availability to technology, including but not limited to the numbers of computers, how often they are accessed, and the quality and speed of Internet connections.	
	Digital Divide – the perceived levels of accessibility to technology according to socioeconomic and geographic factors.	
	The Missouri Department of Elementary and Secondary Education (DESE) is the state agency overseeing public education in the State of Missouri.
	The Missouri Assessment Program (MAP) is a standards-based assessment administered statewide in Missouri by DESE.  The assessment has multiple choice items, constructed response items, and performance events.  Third grade tests include communication arts and science, and fourth grade tests include mathematics and social studies.  
	
	Achievement Level Descriptors for the fourth grade MAP test:
Step 1 – Students add and subtract whole numbers without regrouping; identify congruent shapes; read simple tables, graphs, and charts; identify and complete simple numeric patterns; order positive integers; make comparisons; and identify and classify geometric figures. MAP combined score range: below 568.

Progressing – Students use strategies to solve problems; identify fractions using models; identify values of coins; add and subtract whole numbers; solve monetary problems; compare geometric shapes, rotations, similarity; read Fahrenheit thermometers; use data from displays; use concept of frequency of occurrences; apply probability; create patterns; recognize evens, odds, ordinals, multiples; make logical decisions.  MAP combined score range: 568–613.

Nearing Proficiency – Students identify missing information; multiply whole numbers; determine number sentences and expressions for situations; relate equivalent fractions; identify two- or three-dimensional figures, symmetric properties; read analog and digital time; label points in coordinate system; organize, interpret, predict data; recognize occurrence of likely events; extend patterns; relate operations; and use Venn diagrams.  MAP combined score range: 614–652.
Proficient – Students communicate mathematical processes; determine reasonableness of an answer; solve multi-step problems, spatial problems, logic problems; relate fractions, decimals; compare two- or three-dimensional shapes; measure length, temperature; analyze data displays; describe and extend numeric and pictorial patterns; use number theory; identify, develop algorithms; order like decimals, fractions; construct data displays.  MAP combined score range: 653–691.

Advanced – Students infer from visual models; explore division; relate counting, grouping, place value; use mental computation, estimation; apply spatial relationships; investigate combining, subdividing, changing shapes; apply similarity, congruency, symmetry; construct data displays; determine probability; generalize patterns; use networks, Venn diagrams in problems; and explain subsets, number theory, logic solutions.
MAP combined score range: above 691.

	Enhancing Missouri's Instructional Networked Teaching Strategies (eMINTS) is  a program is administered by The Missouri Research and Education Network (MOREnet) high-speed Internet backbone in collaboration with DESE.  The eMINTS program assists teachers in grades 3-5 in transforming the instructional process by developing student-centered, inquiry based instructional practices.  Students utilize a wide range of multimedia and computer technology to explore interactive learning experiences that require critical thinking skills and problem-solving techniques.  Teachers of eMINTS classrooms are involved in 250 hours of professional development sessions over a two year period, and receive in-classroom coaching and mentoring. Classrooms are equipped with a teacher computer and laptop, a scanner, a color printer, a digital camera, an interactive whiteboard, a high lumen projector, and for every two students one computer that is connected to the MOREnet high-speed Internet backbone.  

Purpose Statement 
	The integration and appropriate use of available technology with the mathematics curriculum may appeal to a variety of learning styles, help to motivate the students, and improve attitudes and achievement.  
		In March of 2002 The Missouri Department of Elementary and Secondary Education (DESE) Division of School Improvement published a technology report – the
  2002-2006 Missouri Education Technology Strategic Plan - that identified 5 major goals and objectives (p. 11):  
1)	Student learning (academic achievement and performance) will be improved through the use of education technologies. 
2)	Teacher preparation and delivery of instruction (performance) will be improved through the use of education technologies.
3)	The teaching and learning process will be enhanced through the use of technology for administration, management, and communications.
4)	All school administrators, teachers, staff, and students will have equitable access to educational technologies that promote student performance and academic achievement.  
5)	All school district administrators, teachers, staff, and students will have adequate technical support.  

	DESE has also adopted The National Educational Technology Standards (NETS) for students, teachers, and administrators published by the International Society for Technology in Education (ISTE).   The standards for students address the following issues as they relate to technology as a tool to help students achieve success in learning, communication, and life skills: basic operations and concepts; social, ethical, and human issues; technology productivity tools; technology communications tools; technology research tools; and technology problem-solving and decision-making tools.
	The Enhancing Education Through Technology Act of 2001 is part of the No Child Left Behind Act signed into law by President Bush in January 2002.  The primary goal of this act is “to improve student academic achievement through the use of technology in elementary schools and secondary schools” (SEC. 2402).  Additional goals seek to ensure technological literacy by the end of eighth grade and to encourage effective integration of technology with staff and curriculum development.  
	
Significance of the Study
	Creighton (2003) states that the mere presence of computers, hardware, and other technological devices does not serve a notable purpose unless it is used effectively to improve academic performance.  Computers should do more than replace other teaching methods.  Many of the recent technology standards imposed on school leaders fail to emphasize that the role of the principal must include an evaluation of how well technology is utilized in schools.  Creighton also identifies three reasons for failure of technology programs: lack of appropriate leadership (too much or too little), proceeding without sufficient and supportive staff development, and failure to get the right people involved.  Teachers need to be involved in researching the effects of technology on learning.  Royer (2002) reports that only twenty percent of our nation’s public school teachers feel comfortable using computers.  Royer offers the explanation that teachers are not yet convinced that technology has the ability to improve learning.  Teachers will strive to implement a program if they believe in it.  Teachers need to know not only how to use technology, but how it can be used to develop understanding, constructivism, cooperative learning, and problem-based learning.  Teachers need to be involved in exploring and testing the use of technology to improve achievement so that they will perceive ownership of the problem and sense a connection to the solution – teachers need to share the vision.  Royer says that the advantages of action research are that it is focused on improving student achievement and it requires participation, commitment, and action.  The continuous cycle of action research begins with a plan, followed by implementation, observation, reflection, and renewal.  

Background of the Problem
	Schools across Missouri focus instruction on improving scores on the statewide Missouri Achievement Program (MAP) achievement tests. The first approach in determining satisfactory  improvement in MAP performance is the index approach.  To acquire all possible points from the index approach Fourth grade Mathematics scores must be equal to or greater than an average of 220 for five years or equal to or greater than 220 for 4 out of the 5 years.  Greenfield Elementary is a small, rural school in Southwest Missouri .   The fourth grade mathematics index scores for Greenfield Elementary School for 1999-2003 averaged 208 with only one year above 220 (see tables 1 and 2).

Table 1: Greenfield Elementary Map Score Achievement Levels for Fourth Grade Math:

Missouri Assessment Program (MAP), 1999 - 2003	
Mathematics	GREENFIELD R-IV 	Missouri	
4th Grade	1999 	2000 	2001 	2002 	2003 	1999 	2000 	2001 	2002 	2003 	
Percent of Students	
Step 1	0 	2.7 	4.9 	0 	0 	3.2 	3 	2.6 	2.7 	2.7 	
Progressing	30.2 	8.1 	24.4 	16.7 	11.1 	18.9 	19.3 	17.8 	18.4 	17.6 	
Nearing Proficient	44.2 	40.5 	53.7 	47.6 	50 	42.6 	41 	41.9 	41.3 	42.6 	
Proficient	23.3 	40.5 	14.6 	31 	36.1 	28.9 	28.7 	29.4 	29.9 	30.6 	
Advanced	2.3 	8.1 	2.4 	4.8 	2.8 	6.4 	8 	8.2 	7.7 	6.6 	
-- Step 1 or Progressing	30.2 	10.8 	29.3 	16.7 	11.1 	22.1 	22.3 	20.4 	21.1 	20.2 	
-- Proficient or Advanced	25.6 	48.6 	17.1 	35.7 	38.9 	35.3 	36.7 	37.7 	37.6 	37.2 	
-- MAP Index*	198.8 	221.6 	192.7 	211.9 	215.3 	208.2 	209.7 	211.4 	210.7 	210.5 	

source: http://www.dese.state.mo.us/schooldata/four/029004/mapmnone.html

Table 2: Percentage of students scores in upper and lower levels; performance index scores:

Building: GREENFIELD ELEM. (4020 ) Grade Span: K-06 
District: 029004 GREENFIELD R-IV 	
9.1 Missouri Assessment Program – Grade 4 	1998	1999	2000	2001	2002	2003	
Math 

-Advanced & Proficient 

-Step 1 & Progressing	
28.3
21.7	
25.6
30.2	
48.6
10.8	
17.1
29.3	
35.7
16.7	
38.9

11.1	
9.1 MAP Performance Index 

-Grade 4 	202.5 	198.8 	221.6 	192.7 	211.9	215.3	

source: http://www.dese.state.mo.us/planning/profile/apr/2002d0290044020.html

Limitations
	Limitations of this study include time constraints (must be complete by spring 2004), timely access to achievement assessment scores relative to Missouri mathematics for the fourth grade, a limited size sample – two fourth grade classrooms with approximately 15 students, and the assumption that the fourth grade students have been randomly and equally placed.  The study was also limited by the availability of funds.  The study made use of existing resources available in the computer lab with no additional software or other expenses.  

Summary
	The information and research that follows will address the following questions:
1.	How does the use of computers affect at-risk children?

2.	How does integrating technology affect student attitudes and achievement?

3.	How can we individualize instruction and assessment with technology?

4.	How can we meet the challenges of accessibility of technology?

	The focus of the information and research is on improving mathematics instruction with the use of technology.

Chapter 2: Review of the Literature

Review of Literature: Section 1 – Computers and At-risk Children

	The problem of academic success for learning and behavior problem children in an urban setting was studied by Laffey (2003).  Specific research questions (p. 427) were:  
•	Can an ICT (interactive computer technology ) treatment affect achievement in mathematics for the children in the primary grades of this urban school?

•	Does risk factor status predict achievement in mathematics in the regular classroom settings and /or the ICT treatment?

•	Does risk factor status predict behavior during the ICT treatment?

An experimental design compared the performance of young, urban, African-American children from low-socioeconomic-status homes.  A group of children that received interactive computer technology (ICT) experiences was compared with a group that did not receive ICT experiences.  
The children were pre-kindergarten, kindergarten, and first grade students.  They were all African-American children living in an impoverished community (95-99% free lunch) in a large city in the Midwest.  They attended a school of nearly 400 students that was in the bottom 5% of state test achievement scores.  During the year of the study none of the students at the school met minimum state standards for performance-based assessment in mathematics, social studies, language arts, and science.   The treatment group received two 25 minute ICT activity sessions each week for a period of 8 weeks.  Children were escorted to the ICT environment, oriented to the task, provided with technical assistance, and evaluated for attention, engagement, and enthusiasm.  A brief interview was conducted once a week after an ICT session.  Mathematics software was aligned to curriculum objectives and included the Jumpstart series, Mighty Math series, and Millie’s Math House.  Both the treatment and comparison groups received the same instruction in the classroom.  The treatment group’s ICT sessions were scheduled during non-academic periods such as recess and PE.  Students were rated by teachers using the Social Skills Rating System, a standardized, norm referenced scale that assesses problem behavior and academic competence.  Two tests of mathematical knowledge with high face validity were developed from district curriculum objectives.  The math tests were given as pretests and posttests.  The results indicate that the treatment group significantly outperformed the comparison group.  Risk-factors also predicted achievement in the classroom settings and in the ICT treatment group.  Within the treatment group, the not-at-risk children performed better than the at-risk group.  The at-risk treatment group outperformed the not-at-risk comparison group.  
Problem behavior traits, such as poor impulse control, noncompliance with authority, and distractibility, associated with problem behaviors may possibly be positively managed within ICT environments. High quality ICT tasks have features (such as immediate feedback, appropriate cognitive challenge, engaging visuals, and the need for focused attention) that may provide at-risk children with an environment in which they can attend, achieve, and develop self-monitoring skills that enable them to meet other academic demands.  The study also demonstrates how academic and behavioral development can be explained by the concepts of scaffolding.  The study is limited by the small sample size, the difficulty in replicating the treatment conditions, and the pullout nature of the program.  The study’s goal had been defined not as an evaluation of a pullout experience, but as an assessment of how the child’s behavior and learning is scaffolds with computer experience.  
		Page (2002) examined the effects of technology-enriched classrooms on students of low socioeconomic status in a study of ten third and fifth grade Louisiana classrooms with populations of low income, mostly minority students. Each of the five experimental treatment classrooms was supplied with a fully trained teacher and an abundance of educational technology equipment, computers, and software.  The results of reading assessments produced mixed results, but the mathematics scores showed significant improvement.  Participation in the technology-enhanced environment also appeared to elevate the self-esteem levels of low socioeconomic elementary students.  

Review of Literature: Section  2 – Integrating Technology

	The Missouri Office of Social and Economic Data Analysis (OSEDA) Evaluation Team Policy Brief (2003) provided an analysis of 2002 Missouri Assessment Program (MAP) results for Enhancing Missouri's Instructional Networked Teaching Strategies (eMINTS) students. Achievement was studied in third grade communication arts and science and fourth grade math and social studies.  This evaluation project has been recognized by U.S. Department of Education as meeting the requirements for "Scientifically-Based Research" outlined in the No Child Left Behind legislation.   Quantitative results compare the eMINTS classrooms with non-eMINTS classrooms, and the differential impacts of eMINTS enrollment of students in three special categories – special education, Title I, and low income students.  Program teachers and principals participated in interviews, focus groups, and surveys during visits from the evaluation team.  Scores are also compared by the eMINTS teacher qualitative categories of Hallmarks of an Effective eMINTS Classroom: emerging, experimental, transitional, proficient, or advanced.  
For the third grade communication arts and science assessment, there were no significant differences between eMINTS and non-eMINTS students.  However, the differences between eMINTS and non-eMINTS students on the fourth grade mathematics and social studies tests are significant.  Fourth grade Title I students in eMINTS classrooms scored much higher on the fourth grade mathematics and social studies tests.  Fourth grade students eligible for free and reduces lunch programs also scored higher in the eMINTS classrooms.  Comparison of third grade MAP scores as correlated to the Hallmarks of an Effective eMINTS Classroom categories shows that teachers rated as “Proficient” show remarkably significant differences, scoring an average of at least 13.18 points higher than other eMINTS classrooms. Evidence also suggests that any application of inquiry based instructional techniques resulted in a substantial increase in MAP scores.  Differences of test scores for fourth grade teacher Hallmarks are also significant.  
The results support the goals of the eMINTS Professional Development program – improvements resulted on all levels for teachers “whose instructional practice was ‘proficient,’ teaching practices that consistently demonstrated the integration of inquiry-based practices into instruction and made use of the available technology” (p 27).  One implication of these findings for instructional practice is “direct positive relationship between teacher adoption of student centered instructional practices in eMINTS settings and increased student performance” (p 28).   The report also claims to document “the positive impact of inquiry-based instructional methods in a technology-rich environment” (p28).
 	The OSEDA evaluation does not discuss the possibility that the professional development training in the area of student-centered, inquiry-based learning for the eMINTS teachers may be the primary reason for MAP scores improvements, rather than the use of technology in itself as a reason for improving achievement.  It is conclusive that for any technology integration project to function effectively, teachers must be comfortable with the technology and implement it successfully.
	Other studies have found increases in student achievement as a result of integration of technology.  Middleton and Murray (1999) studied the relationship of the degree of technology integration and achievement scores in the fourth and fifth grades.  The Levels of Technology Implementation (LoTi) Instrument determined teachers’ perceived technological expertise.  Student achievement was assessed by the Metropolitan Achievement Tests, a norm-referenced standardized test developed by the Psychological Corporation of Harcourt Brace Jovanovich.  The analysis of variance procedures found no significant difference at the fourth grade level. However, significant positive differences in reading and math achievement were found in the fifth grade, where there was a higher level of technology usage. 	
	Sherry, Billing, Jesse, and Watson-Acosta (2001) report on the WEB Project, a five year Technological Innovation Grant.  Six academic content areas – art, music, social studies, language arts, and interdisciplinary studies – were incorporated with multimedia technology. Their interpretation of the results is that motivation, metacognition, and inquiry learning are significantly correlated, implying that enthusiasm for technological learning experiences may stimulate strategic thinking processes.  Measurements of surveys and student achievement indicated increases in student performance as a direct result of technology supported learning engagements.  
	Waxman, Connell, and Gray (2002) conducted a meta-analysis to determine the extent of empirical evidence that illustrates how teaching and learning with technology affects student outcomes. They proclaim that the results are similar in limitations and similar in outcome to other recent meta-analyses that studied instructional technology.  The results show a “modest, positive effect of teaching and learning with technology on student outcomes” (p12).  They were encouraged by an extensive list of variables, suggesting that teaching and technology interact, or interact with related features or processes, to positively impact outcomes.

Review of Literature: Section 3 – Individualization

	Rose and Meyer (2002) are cofounders of the  Center for Applied Special Technology (CAST), a not-for-profit organization founded in 1984 whose original mission was to “develop and apply technologies that would expand learning opportunities for individuals with disabilities” (p. v).  Since its establishment, CAST has begun a paradigm shift to a new position of “the use of technology to transform the nature of the curriculum itself” (p. v).  After fifteen years of thought, research, and collaboration with individuals and educational communities, the organization has developed Universal Design for Learning (UDL), a research-based set of principles that outline a practical structure to maximize learning opportunities with the assistance of technology.  This program is based on the realization that barriers to learning are not an intrinsic component of the learner, but these barriers emanate from interactions with inflexible instructional methods and materials.  Based on brain and media research, the principles of UDL allow educators to teach all students by developing appropriate learning goals, teaching methods, materials, and fair, authentic assessments.  The opportunity brought on by the rapid evolution of technology has propagated the creation of flexible instructional methods and materials that have the potential to communicate with diverse learners.  The vision of UDL is to provide a framework that allows many different educational innovations and approaches to incorporate new ideas about the use of technology to improve learning.  One highly relevant example comes from Armstrong (2000), who provides an extensive list of computer “software that activates the multiple intelligences” (p. 122).  Armstrong also comments on the use of various intelligences for technology related tasks: linguistic intelligence is used for word processing, intrapersonal intelligence is used for a multimedia project, interpersonal intelligence is used for a cooperative project, and bookmarks may be used for Web pages related to each of the intelligences.  
	Within the computer-like, multifaceted, dense brain network of 1 trillion neurons connected by 10 trillion connections, Universal Design for Learning (Rose and Meyer, 2002) identifies three primary interconnected brain networks specialized for learning tasks.  These networks – recognition, strategic, and affective – parallel Russian scientist Lev Vygotsky’s three prerequisites for learning – recognition, application, and engagement.  Learners differ in each network, giving them a uniqueness that cannot be simplified to a disability or giftedness.  While instructional methods tend to focus on one network, all three brain networks are involved in learning.  The flexibility of digital media grants the ability to attend to the diverse needs of students in an assortment of educational contexts.  This flexibility is inherent in the abstract, versatile, transformable, and networkable storage and transmission capacity of digital media. 
	Universal Design for Learning (Rose and Meyer, 2002) draws on brain and media research to propose the three kinds of flexibility that educators should strive for (p. 69):
1)	To represent information in multiple formats and media.
2)	To provide multiple pathways for students’ action and expression.
3)	To provide multiple ways to engage students’ interest and motivation.  

The diversity and flexibility that UDL provides allows individualization on a level unobtainable with traditional teaching materials and methods.  
	Universal Design for Learning (Rose and Meyer, 2002) also provides educators with the possibility of accurate, individual assessments.  Traditional assessments do not account for individual learning differences, the incompatibility of testing media, the lack of or removal of supports and scaffolds, and the lack of assessment integration with curriculum.  UDL allows for flexibility in presentation, expression, strategic supports, and engagement.  Standardized tests provide a valuable means of evaluating trends and knowledge about groups, but lack the ability to accurately assess individual student skills, knowledge, and learning.  New assessments of the digital age will be dynamic, universally designed, and allow for two-way interaction.  The resulting learning environment will provide more effective curriculum and teaching methods.
	
Review of Literature: Section 4 – The Challenges of Accessibility of Technology
	Creighton (2003) warns that school and technology leaders need to be aware of the “digital divide” and take steps to see that it is bridged.  Surveys and studies have shown that access to technology, computers, and the Internet is widely varied depending upon geographical and socioeconomic factors, as well as ethnicity, gender, and achievement levels.  The principal should strive to provide an environment that promotes equal opportunity access to technology.
	The United States Department of Education (ED) (2003) states that parents and educators consider technology to be an important part of a high quality education.  The concern is that not all student shave equal access to educational technology.  The availability and successful integration of technology is especially a priority in rural schools and schools with high populations of minority or poor students.  The federal government funds programs designed to provide equitable access and encourage the effective use of technology in educational settings.
The Integrated Studies of Technology (ISET) is a set of studies of state, district, school, and teacher surveys to provide information on federal funding of educational technology.  ED contracted with three institutes to provide this information.  Results of their reports are summarized in the paragraphs that follow.
	One ISET study was conducted by SRI International (2002) on the Professional Development and Teachers Use of Technology.   The evaluation of the role of professional development found that teachers needed more training and more time to participate in educational technology related activities.  Most teachers participated reported participating in several professional development activities, of which formal and informal forms of training appear to influence the use of technology.  However, the quality of the professional development is inconsistent with most teachers reporting little time to practice the new skills.  Most teachers rated themselves as somewhere in between extremely weak and extremely proficient in the ability to use technology for instructional purposes, and said that the greatest need for training was on how to integrate technology with instruction rather than basic computer skills.
	Another ISET study was conducted by the American Institute for Research (2002) on Implementing the Technology Literacy Challenge Fund Educational Technology State Grants Program.  The first federal assistance foe enhancing educational technology was the Technology Literacy Challenge Fund (TLCF), which awarded $1.93 billion to states and territories between 1997-2001.  The TLCF program allows great flexibility in allowing the states to award subgrants to districts for improving Internet connectivity, professional development, and for promoting integration of technology in the classroom.  Priority for awards were based on economic need or the need for developing educational technology.   The report indicated that states designed the subgrants to reflect state priorities, with no one priority being dominate, and multiple priorities for awards in many states.  States targeted high poverty and small, rural districts, while some states gave large awards, others more numerous, smaller awards.  More than half of the districts that did not apply for funds said they did not know about the program, indicating that states may need to have more comprehensive outreach techniques.  The TLCF program emphasized the accessibility of educational technology, with most teachers reporting two or more computers in a classroom or computer labs with 25 or more computers.  While most schools reported connections to the Internet, urban, large districts, and high poverty districts had fewer classroom connections.  Barriers to the use of technology were reported as limited availability of useful software and Internet sites, limits in student technology skills, and access to technology outside of skill.  
	 The third ISET study was A Formative Evaluation of the E-Rate Program by the Urban Institute (2002).  The E-Rate program was created by Congress as part of the Telecommunications Act of 1996 to bridge the “digital divide” that exists in American society.  This federal program supports broader public access to new digital technology, especially for the poor, minorities, and the geographically isolated.  This study analyzes the extent to which the E-Rate program is able to equalize digital technology access for those eligible to receive the program discounts available for the acquisition of digital technology and the necessary infrastructure for its effective use.  The study also focuses on the ability of schools and teachers to use the supported technology in the classroom.  Improvements that were noted included the number of schools connected to the Internet, the speed of Internet connections, and the ratio of connections per student.  Most teachers in schools served by E-rate have access to email and are able to access the school’s computer network and Internet from home.  Students and teachers in E-Rate schools that are in poorer districts are likely to encounter circumstances limiting their use of technology for instruction, such as inadequate teacher skills, facility limitations, and Internet speed and reliability.  Students in rural school face similar restrictions from lack of general technology skills and technical support staff, while urban districts lack technological skills, technical support, building electrical systems, and Internet speed and reliability.  Large E-Rate districts are also limited by the amount of training available for teachers, the number of computers available for instructional purposes, technical support, building facilities, and Internet connectivity factors.   This study suggests progress is being made, but improvement may be needed in the efficiency, flexibility, and coordination of the program.
		It will be interesting to watch how the great flood of technological innovations will change curricula, teachers, pedagogies, students, texts, assessments, learning styles, and entire educational communities in coming years.  It may at times be exciting, perhaps frustrating, dangerous, explosive, discombobulating, mind boggling, or stormy.  

Chapter 3: Research Methods

Sample
 	The sample will be one of two fourth grade classroom in a small, rural school in Southwest Missouri.  The control group will be the other fourth grade classroom in the same school.  The number of students to be evaluated will be 12 in the treatment group and 15 in the control group.

Instrumentation
 	Because of the low number of students sampled, the students will be given a pretest in September 2003, and the same test will be repeated after treatment in April 2004.  The assessment will be a standardized type assessment available to Internet subscribers at www.edhelper.com. 

Materials
	The computer lab that will be utilized includes 25 computers and a projector.  Students will visit Internet web sites at AAA Math (www.aaamath.com) and The National Library of Virtual Manipulatives for Interactive Mathematics (http://matti.usu.edu/nlvm/nav/index.html).  The AAA Math wed site contains basic mathematical operations that are illustrated and have interactive problems and exercises.  The National Library of Virtual Manipulatives for Interactive Mathematics is a three-year National Science Foundation supported project to develop a library of uniquely interactive virtual manipulatives for mathematics instruction.  
	
Design
	The design for the achievement assessment will be pretest, treatment, and posttest.  Microsoft Excel software will be used to determine if there is a significant difference in the means of test scores.  A dependent t-test will look at each group’s pretest and posttest scores.  An independent t-test compare posttest scores of both groups.  

Procedures
	Students in the treatment group will receive instruction and activities in the school computer lab for 60 to 90 minutes per week.   Most of the computer instruction will be on Internet sites such as AAA Math (www.aaamath.com) and The National Library of Virtual Manipulatives for Interactive Mathematics (http://matti.usu.edu/nlvm/nav/index.html)  which have lessons designed for fourth grade level.  Both the treatment and control group will receive textbook instruction with the Saxon Math series.

Summary
	The purpose of the research is to improve mathematics instruction and achievement using technology.  The treatment group and control group will be evaluated in September and October to determine if the computer instruction resulted in any improvement.  Pretest and posttest mean scores will be compared using statistical software.  The results will be presented as tables and charts with a summary of findings.

Chapter 4: Analysis of the Data
test group	
pretest	posttest	
121	168	
107	155	
72	127	
77	115	
99	162	
73	138	
87	150	
104	156	
121	179	
125	164	
130	179	
83	132	

Table 3A: t-Test: Paired Two Sample for Means test group	
	pretest	posttest	
Mean	99.92	152.08	
Variance	452.08	416.08	
Observations	12.00	12.00	
Pearson Correlation	0.91		
Hypothesized Mean Difference	0.00		
df	11.00		
t Stat	-20.21		
P(T<=t) one-tail	0.00		
t Critical one-tail	1.80		
P(T<=t) two-tail	0.00		
t Critical two-tail	2.20	 	

Dependent t-test and Independent t-test

Table 4A: t-Test: Paired Two Sample for Means control group	
 	pretest	posttest	
Mean	92.80	148.20	
Variance	850.17	537.89	
Observations	15.00	15.00	
Pearson Correlation	0.79		
Hypothesized Mean Difference	0.00		
df	14.00		
t Stat	-12.00		
P(T<=t) one-tail	0.00		
t Critical one-tail	1.76		
P(T<=t) two-tail	0.00		
t Critical two-tail	2.14	 	

control group	
pretest	posttest	
59	149	
137	178	
65	101	
113	171	
112	166	
142	164	
79	145	
129	174	
63	120	
57	134	
85	158	
89	157	
72	132	
77	115	
113	159	

Table 5: Independent t-Test results 		
	posttest
test group	posttest
control group	
Mean	152.08	148.2	
Variance	416.08	537.893	
Observations	12	15	
Pooled Variance	484.29		
Hypothesized Mean Difference	0		
df	25		
t Stat	0.46		
P(T<=t) one-tail	0.33		
t Critical one-tail	1.701		
P(T<=t) two-tail	0.65		
t Critical two-tail	2.06	 	

Table 3B

Table 4B

Table 6:Test Group: Pretest Scores Means	
computation	concepts	problem solving	application	geometry	measurement	data/graph	Total	
28	26	20	11	13	16	7	121	
23	22	24	10	7	13	8	107	
18	19	10	7	3	10	5	72	
26	12	14	6	5	11	3	77	
23	24	18	9	6	12	7	99	
16	15	13	9	7	10	3	73	
18	18	14	11	7	12	7	87	
26	22	18	12	6	15	5	104	
24	26	24	13	11	15	8	121	
25	29	22	14	13	14	8	125	
25	29	19	13	13	23	8	130	

means

16	22	12	8	9	13	3	83	
22.3	22.0	17.33	10.25	8.33	13.67	6	99.92	

Table 7: Control Group: Pretest Scores Means	
computation	concepts	problem solving	 application	geometry	measurement	data/graph	Total	
14	13	10	6	5	7	4	59	
33	25	28	13	12	19	7	137	
15	14	13	10	2	8	3	65	
25	27	21	10	6	18	6	113	
18	31	18	10	10	17	8	112	
32	31	29	12	11	20	7	142	
14	10	18	12	6	12	7	79	
30	30	25	14	8	14	8	129	
11	21	11	8	3	8	1	63	
18	16	5	4	3	5	6	57	
25	19	13	8	8	9	3	85	
23	16	15	12	7	10	6	89	
15	20	15	8	5	8	1	72	
20	20	14	6	6	9	2	77	

means

24	23	22	14	9	14	7	113	
21.13	21.06	17.13	9.8	6.73	11.87	5.06	92.8	

Table 8: Test Group: Posttest Scores Means	
computation	concepts	problem solving	 application	geometry	measurement	data/graph	Total	
35	37	33	14	19	23	7	168	
38	34	32	14	15	15	7	155	
37	26	27	9	8	14	6	127	
33	22	24	10	9	11	6	115	
34	34	33	14	18	22	7	162	
36	31	25	13	11	14	8	138	
34	33	31	13	14	18	7	150	
38	32	30	15	17	17	7	156	
38	38	38	15	17	26	7	179	
39	38	34	11	13	21	8	164	
39	38	37	16	17	25	7	179	

means

33	32	28	11	11	13	4	132	
36.17	32.91	31.0	12.9	14.08	18.25	6.75	152.08	

Table 9: Control Group: Posttest Scores Means	
computation	concepts	problem solving	 application	geometry	measurement	data/graph	Total	
38	35	25	12	14	19	6	149	
38	36	35	15	19	27	8	178	
34	16	18	9	9	10	5	101	
38	38	36	14	15	22	8	171	
40	35	33	13	15	23	7	166	
40	34	33	12	18	21	6	164	
31	31	30	14	12	21	6	145	
38	36	37	15	15	26	7	174	
37	20	17	13	10	17	6	120	
37	24	30	12	12	11	8	134	
37	32	33	15	15	18	8	158	
37	31	36	14	14	18	7	157	
37	27	29	11	7	14	7	132	
35	18	24	10	9	12	7	115	

means

39	29	34	15	15	19	8	159	
37.07	29.46	30.0	12.93	13.27	18.53	6.93	148.2	

Chart 1: Test means for subtest:

Table 10:
Test Group: t-Test: Paired Two Sample for Means	
 	pretest	posttest	
Mean	99.92	152.08	
Variance	452.08	416.08	
Observations	12.00	12.00	
Pearson Correlation	0.91		
Hypothesized Mean Difference	0.00		
df	11.00		
t Stat	-20.21		
P(T<=t) one-tail	0.00		
t Critical one-tail	1.80		
P(T<=t) two-tail	0.00		
t Critical two-tail	2.20	 	
test group	test group	
pretest	posttest	
121	168	
107	155	
72	127	
77	115	
99	162	
73	138	
87	150	
104	156	
121	179	
125	164	
130	179	
83	132	

Table 11:
Control Group: t-Test: Paired Two Sample for Means	
 	pretest	posttest	
Mean	101.08	151.67	
Variance	706.08	580.79	
Observations	12.00	12.00	
Pearson Correlation	0.83		
Hypothesized Mean Difference	0.00		
df	11.00		
t Stat	-11.55		
P(T<=t) one-tail	0.00		
t Critical one-tail	1.80		
P(T<=t) two-tail	0.00		
t Critical two-tail	2.20	 	

control group	control group	
pretest	posttest	
137	178	
65	101	
113	171	
112	166	
142	164	
79	145	
129	174	
85	158	
89	157	
72	132	
77	115	
113	159	

Summary of Findings
	Tables 3 and 4 show the pretest and posttest means for the test (treatment) group and the control group.  The pretest means for the test group (99.92) is considerably higher than the pretest means for the control group (92.80).  The posttest means for the groups are much closer at 152.05 for the test group and 148.20 for the control group.  Table 5 show a comparison of the posttest scores for both groups and shows no statistical difference in the means of the posttest scores. 
	The control group had three extremely low pretest scores and relatively high posttest scores, marked in blue on Table 4B.  When these scores and the posttest scores are removed from the calculations, the pretest and posttest scores are more similar:

Table 12: Complete test and control group:
	pretest means	posttest	
test group	99.92	152.08	
control group	92.80	148.20	

Table 13: Test group and control group with the three 
extremely low pretest scores removed from the control group:
	pretest means	posttest	
test group	99.92	152.08	
control group	101.08	151.67	

Is this situation, the control group starts at a slightly higher pretest means , but is slightly lower than the test group on the posttest means.  One possibility: The students were not assigned a classroom grade for the pretest, but the posttest was assigned a classroom grade.  Some students who were aware of this that did not put forth effort on the pretest may have put forth increased effort on the posttest.  (Students were told the pretest would not be graded.  They were not informed that the test would be repeated later.  On the posttest the students were informed that it would be graded.)  
	Tables 6, 7, 8, and 9 show how students scored when the subtest scores were calculated for the seven subtests: computation, concepts, problem solving, application, geometry, measurement, and data/graphing.  The test group scored higher in all subtest on the pretest, but scored higher on only three of the seven subtests on the posttests, yet retained an overall higher means score.  The categories of subtests that the test group scored higher on included concepts and problem solving, two categories that likely received more attention in the treatment.  

Chapter 5 : Action Summary

	This portion of the action research project did not show any significant findings for the improvement of mathematics instruction with technology.  Two primary reasons are likely.  The limitations of the study included a very small population.  Additionally, the primary method of technological instruction with time and technology available was more drill and practice than constructive methods of instruction.  The action research also did not include a method of instructing and assessing special needs and at-risk groups, a definite area to be looked at concerning the use of technology.  A staff survey at Greenfield Elementary School indicated a need for improvements in technology and professional development related to technology issues.
	The next phase of this action research project will strive to sample a larger population and a more constructive approach.  Constructivism as it relates to technology is outlined in the following pages.
	Technology has the potential to be used as a tool to support and encourage constructivist methods of learning. In constructivism, rather than disseminators of knowledge, teachers become facilitators for an active process of construction of knowledge by the student.  Creighton (2002) identifies five concepts (p. 46) that be used to evaluate the extent of constructivism in the use of technology in the classrooms: 
1.	Students use of creative abilities to solve real world problems.
2.	Collaboration with students on the means of instructional strategies and the content.
3.	Identification of two or three main ideas to be explored, or uncovered rather than 	covered.
4.	Social discourse and interaction.
5.	The teacher is a guide and presenter of problems and tasks.
		
	Creighton (2002, pp. 49 – 51) promotes 12  descriptors of constructivists teachers outlined by Grennon Brooks and Martin Brooks (1999) in the book In Search of Understanding: The Case for Constructivist Classrooms.  
Constructivist teachers:
1.	encourage and accept student autonomy and initiative.
2.	use raw data, primary sources, and manipulative, interactive, and physical materials.
3.	use cognitive terminology such as classify, analyze, predict, and create.
4.	allow student responses to drive lessons, shift instructional strategies, and alter content.
5.	inquire about students’ understanding of concepts before sharing their own.
6.	encourage students to engage in dialogue with each other and the teacher.
7.	encourage student inquiry by asking open-ended questions and encouraging students to 	question each other.
8.	seek elaboration of initial responses of students.
9.	engage students in experiences that may foster contradictions to initial hypotheses.
10.	  allow wait time after posing questions.
11.	  provide time for students to create relationships and metaphors.
12.	  nurture students’ natural curiosity 
By moving the discovery step to the beginning, students of all abilities are given the opportunity to experiment, hypothesize, and discover, something that traditional teaching saves for last with only the brighter students  participating.  
	Creighton (2003) describes five types of actors, as they apply to technology, in change processes that Phillip Schlechty first identified in 1997: 
1. Trailblazers – those who will willingly venture in to the implementation of technology.
2.  Pioneers – need assurance that the program will be worth the effort.
3.  Settlers – need more detail and specific direction.
4. Resistors – satisfied with the status quo.
5.  Saboteurs – committed to stopping new ideas.
Common reasons for resistance and sabotage include a lack adequate skills for technology complicated by embarrassment and reluctance in admitting the perceived inadequacy.   Others may not believe that technology has any benefit or value to support teaching and learning.  Some may even fear that teaching or other staff positions may be replaced if technology programs are implemented.  
	Creighton (2003) emphasizes that it is important to involve as many stakeholders as possible in the implementation of technology, including resistors and saboteurs.  A collaborative effort should be made to include teachers, parents, students, board members, and area business partners.  Teachers may have the power to move a good plan forward, and may choose to do so if the plan focuses on benefits to the curriculum.  Extensive teacher education must address the aspect of using technology to improve teaching and learning. Teacher evaluation as it relates to  technology should have a focal point that looks for meaningful, engaged learning.  The teacher becomes a facilitator, a guide, a co-learner, and a co-investigator, while the student becomes an explorer, a cognitive apprentice, and a producer of knowledge.  The use of technology needs to be built on the constructivist philosophy for productive, meaningful and effective learning to occur.  
	The Enhancing Missouri's Instructional Networked Teaching Strategies (eMINTS) program is based on a constructivist philosophy (Evaluation Team Classroom Observation Report, 2001):
“The main purpose of the eMINTS program as a whole has been to develop constructivist, ‘inquiry-based’ teaching practices among the participating teachers. This purpose has realized through the creation of technologically intensive classrooms and the provision of extensive professional development. The professional development effort has focused ways to create a classroom environment that facilitates student inquiry, higher-order thinking skills and
interdisciplinary student work.” 
A technology formatted constructivist lesson plan form is available at the eMINTS web site: http://emints.org/resources/constructivistlesson-form.pdf.
	The United States Department of Education's "Scientifically-Based Research" outlined in No Child Left Behind has recognized the eMINTS evaluation as meeting the requirements for this legislation, and eMINTS was featured as a model program at the State Educational Technology Directors Association (SETDA) "National Leadership Institute on Evaluation" in December 2002.  SETDA commissioned a study by the Metiri Group (Lemke, 2004) released just this year on No Child Left Behind Title II D: Year One in Review.  This study includes competitive grants that were used to supplement the eMINTS program.  The study lists 6 major findings:
1.	A Shift in Emphasis from Technology to Learning. 
2.	The Focus Is on Professional Development. 
3.	Doing More with Less through Collaborations and Partnerships. 
4.	Title II D Formula Grants Sustain Existing Programs. 
5.	Evaluating Effectiveness Requires State Leadership.
6.	A Knowledge Base Is Emerging.

These findings are consistent with other projects previously referenced.

References

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