2.1 Antecedentes de la investigación
2.2.2. Estrategias de Aprendizaje
42.30% 19.20% 38.50% Suburban Rural Urban LaSIP Teachers 28.9% 13.2% 57.9% Urban Rural Suburban
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Figure 16
Gender of non-LaSIP Teachers
Figure 17
Gender of LaSIP Teachers
LaSIP Teachers GENDER Female Male Number 40 36 32 28 24 20 16 12 8 4 0
non-LaSIP Teachers GENDER Female Male Number 24 22 20 18 16 14 12 10 8 6 4 2 0
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Teachers were also asked to indicate the type of school district where they worked as urban, suburban or rural as seen in Figures 14 and 15. Teachers in the LaSIP group were made up of fifty-eight percent (58%) urban educators, thirteen-percent (13%) suburban educators and twenty- nine percent (29%0 rural educators. The Non-LaSIP teachers were equally split between urban and rural teachers, forty-one percent to forty-one percent with eighteen percent (18%) from suburban districts.
As indicated in figures 16 and 17, males made up a smaller percentage of teachers than females. The difference between male and female respondents was greater in the LaSIP group than in the Non-LaSIP group. When expressed as percentages, females made up eighty-five percent (85%) of the Non-LaSIP group and ninety-five percent (95%) of the LaSIP group.
Part II. Overview of Statistical Procedures Used to Answer Research Questions. Part II of the study addressed in depth reporting of results for Sections A-G of the survey and
transcription and analysis of the five open-ended questions in Section H of the survey, personal interviews and focus group discussions. Respondents to the survey indicated their perceptions of 71 close-ended questions on five point Likert scales and provided written responses to five short answer questions. The participants who consented to personal interviews and participation in focus group discussions also completed surveys.
Internal consistency of the survey subscales was measured using Cronbach’s coefficient alpha which is typically a measure of the correlations between different items on the survey or subscales of the survey. The scores in Table 3 represent Cronbach’s coefficient alpha results for subscales A-G of the survey. Values of alpha vary between 0 and 1. An alpha reading of 0.7 and above is considered an acceptable measure of internal consistency (Hair et al, 2006). An alpha greater than 0.7 was obtained for all the subscales indicating acceptable internal consistency.
135 Table 3.
Reliability of Survey Subscales
Sub-Scale Number
of Items
Alpha A. Features of the LaSIP Professional Development Program 11 .8879
B. Follow-up Activities 9 8969
C. Context 11 .8820
D. Implementation of a Reform-Based Curriculum 6 .7830 E. Implementation of Reform-Based Teaching Strategies 14 .7309 F. Effect of Reform-Based Strategies on Student Achievement 14 .7574 G. Practical Benefits of Professional Development Programs 6 .7592
The data obtained from the survey was analyzedand used to provide answers or partial answers to the five research questions proposed in this study. The open-ended questions 72-76 in Section H were recorded case wise and subjected to analysis along with transcripts of personal interviews and focus group discussions. Sections A-G of the survey was subjected to statistical analysis using the Statistical Package for Social Sciences (SPSS), version 9. For example, the responses to the 11 items in Section A of the survey were subjected to principal component analysis using ones (1s) as prior communalities estimates. The principal component method was used to extract the initial factors with eigenvalues greater than 1 which resulted in three factors that accounted for 68% of the variance. Examination of the scree plot also suggested that there were three factors of importance. Hence the first three components were retained and subjected to varimax (orthogonal) rotation. The results of the rotation with a list of survey items 1-11 and corresponding factor loadings for Section A are shown in Table 4.
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Although factor loadings of 0.3 or higher are acceptable according to (Leech, Barrett & Morgan, 2008), factor loadings of 0.4 or higher were retained and used in regression analysis in this study. In Section A of the survey, Items 4, 7 and 11 loaded highest on Factor 1. Items 3, 5, 6 and 9 loaded highest on Factor 2. and Items 1, 2, 8 and 10 loaded highest on Factor 3. The three factors were then saved and used to represent three composite variables. Component 1 made up of items 4, 7 and 11 is a measure of features of the LaSIP program that dealt with the how (process) and what (content) of science teaching. The composite variable name was shortened to PROCONT. Items 3, 5, 6 and 9 loaded highest on Factor 2.
Table 4.
Principal Component Analysis Section A Features of the LaSIP
Components/Loadings Questions 1 PROCON T 2 MODLING 3 TIMPRAC
Features of my professional development experiences that were most influential in improving teaching and learning and contributing to my use of the training experiences in the classroom included:
0.503 0.676 1. Sufficient time for acquiring the pedagogical content knowledge to implement the concepts and strategies in a classroom setting.
0.418 2. Emphasis on standards-based teaching and learning. 0.796 3. Time for reflection and writing about teaching
and learning experiences.
0.815 4. Activities that emphasized the use of science process skills.
0.565 0.608 5. Instruction in alternative assessment that included models of authentic, real-life experiences.
0.800 6. Modeling teaching and learning strategies during microteaching activities.
0.808 7. Emphasis on learning major science concepts. . 0.796 8. Time to practice research-based teaching strategies.
0.654 0.589 9. Opportunities to learn through a variety of methods
. 0.444 0.563
.
10. Attention to learning styles and multiple intelligences that were useful in classroom instruction.
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These items dealt with modeling of research-based teaching strategies and alternative assessment. The composite variable was renamed MODLING. Items 1, 2, 8 and 10, which loaded highest under Factor 3, were measures of the time afforded participants to practice research-based teaching strategies and to acquire needed pedagogical content knowledge. The third component was renamed TIMPRAC. The results of data reduction via principal component analyses are shown in Tables 4 -10. Composite variables were used as independent variables in regression analysis to determine the predictive value of the variables for the dependent variable RBTS. Table 5
Principal Axis Factoring Analysis Section B. Follow-Up
Section B of the survey was used to investigate teacher perceptions of the nature of program follow up activities. Items 12-20 were subjected to data reduction. Principal axis factoring was the extraction method used in analysis of this section of the survey. Two factors were extracted that accounted for 63% of the variance. The factor matrix was subjected to
Factor Loadings Questions
1 COLLABOR
2 HANDEXP
My professional development experiences included follow-up activities that provided opportunities for:
0.735 0.497 12. Additional instruction and practice.
0.662 0.533 13. Sharing of resources and expertise with colleagues and fellow participants.
0.868 14. Exchange of ideas through visitation to other participants classrooms.
0.556 15. Presentation and sharing the results of research with colleagues.
0.723 16. Acquisition of resources for classroom instruction.
0.645 0.540 17. Site visits by course teachers or program coordinators and staff.
0. 951 18. Hands on experiences with materials and supplies.
0.645 19. Training for administrators in systemic educational reform. 0.487 20. Active support from the principal in implementing new
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varimax rotation. The resulting rotated factor matrix is shown in Table 5. The two composite factors were renamed HANDEXP and COLLABOR and used in regression analysis.
Table 6
Principal Axis Factoring Analysis Section C. Context
Factor Loadings Questions
1 SCDSUP
2 COLLAB
School and District factors that have an impact on classroom implementation of research-based strategies include:
0.442 0.569 21. Discussions with other teachers in the school district about successful standards-based teaching strategies facilitates implementation of new teaching and learning.
0.652 22. Collaboration with colleagues in the school/district has helped to improve my teaching and assessment skills.
0.793 23. Having common planning time with other teachers rained to use research-based teaching strategies helps me to implement new ideas from professional development experiences.
0.512 24. My principal is supportive of my efforts to implement new standards-based teaching strategies.
0.543 25. Parents understand and support my use of new teaching strategies and alternative assessment methods.
0.587 26. The school provides ongoing technical support for implementation of standards-based teaching and learning.
0.780 27. The school district provides ongoing financial support for implementation of standards-based teaching and learning. 0.748 0.416 28. The district has adopted a standards-based curriculum and
encourages teacher participation in job-embedded professional development.
0.733 29. Overall school climate at my school is not conducive to implementation of reform-based teaching practices.
0.430 0.450 30. Increased time for planning has helped me to implement reform- based teaching and learning in the classroom.
0.646 3 31. Ongoing technical assistance offered by the school district.
Section C Context of the survey was subjected to principal axis factoring using varimax rotation with Kaiser normalization. The two factors accounted for 51% of the variance. The rotated factor matrix is shown in Table 6. The two composite variables were renamed SCDSUP and COLLAB and saved for use in linear regression analysis.
139 Table 7
Principal Component Analysis Section D. Implementation of a Reform-based Curriculum
Participants were asked to indicate their beliefs concerning implementation of a reform- based curriculum in Section D of the survey. Section D was subjected to both principal axis factoring and principal component analysis. The two components extracted using principal component analysis accounted for 65 % of the variance while the two factors extracted using principal axis factoring accounted for 51% of the variance. A study of correlation matrices indicated that the factors were uncorrelated. The values shown in Table 7 are the results of principal component analysis using varimax rotation. Components were saved as variables and used in regression analysis.
Section E of the survey asked participants to indicate frequency of use of research-based teaching strategies in their classrooms. Section E was subjected to data reduction via principal component analysis using direct obliminal rotation. Results are shown in Table 8.
Component Loadings Questions
1 BELREF
2 NOEQUIP
Indicate your beliefs concerning implementation of a reform-based curriculum in your classroom. 0.561 32. Promotes life-long learning.
0.685 33. Requires equipment, supplies and technological resources that most schools cannot afford.
0.710 34. Can be accomplished by any classroom teacher.
0.853 35. Is needed to help students achieve state and national standards.
0.659 36. Incorporates strategies that can help students (including students with special needs) succeed, academically. 0.859 37. Can better meet the needs of students than traditional
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The two factors extracted were renamed RBTS for the 10 research-based teaching strategies and NONRBTS for the 4 non-research-based teaching strategies. The variables were saved and used in regression analysis.
Table 8
Principal Component Analysis Section E Implementation of RBTS
Component Loadings Questions 1
RBTS
2 NONRBTS
Indicate the frequency of implementing the following strategies in classroom instruction:
0.440 37. Identifying similarities and differences.
0.524 38. Teaching science as inquiry.
0.770
40. 40. Lecture and /or lecture demonstration.
41.
0.711 41. Hands-on science experiments.
0.704 42. Thinking maps and other graphic organizers.
0.778 43. Cooperative learning.
44.
0.502 44. Drill and practice.
0.612 45. Alternative assessments such as portfolios and exhibits.
< 0.400 46. Reading aloud from the textbook. < 0.400 47. Reflective logs and journals.
0.679 48. Long term science investigations
0.601 49. Writing about science.
0.658 50. Worksheets.
141 Table 9
Principal Component Analysis Section F. Effect of Implementation of RBTS on Student Achievement Components Questions 1 LABRBTS 2 GENRBTS 3 NONRBTS
Indicate how your use of each of the following teaching strategies has affected student
achievement in your classroom:
0.814 52. Identifying similarities and differences.
0.782 53. Teach science as inquiry.
< .4 54. Lecture and /or lecture demonstration.
0.865 62. Long term science investigations or class projects
0.682 55. Hands-on science experiments
0.491 0.588 56. Using thinking maps and other graphic organizers .
0.634 57. Cooperative learning.
0.766 58. Drill and practice.
0.521 0.566 59. Alternative assessments such as portfolios and exhibits.
0.772 60. Reading aloud from the textbook. 0.638 61. Reflective logs and journals.
0.761 62. Long-Term Science Investigations
0.761 63. Writing as a tool to increase comprehension and
thinking 0.667 64. Worksheets.
0.694 65. Focusing on higher order thinking skills.
Section F of the survey asked participants to indicate their perceptions of the effects of using research-based teaching strategies on student achievement. Results were subjected to data
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Table 9. The components were renamed LABRBTS for lab related research-based teaching strategies, GENRBTS for general research-based strategies and, NONRBTS for none research- based teaching strategies. The three components were saved as variables and used in regression analysis.
Table 10.
Principal Component Analysis Section G. Practical Benefits
Components/Loadings Questions 1
PRACBEN
2 GRADTEAM
Describe your agreement concerning the influence of the following benefits on your choice or attendance of professional development activities.
0.565 0.459 66. Receiving graduate credit.
0.780 67. Acquisition of free equipment and supplies. 0.815 68. Receiving a stipend for participation.
0.718 . 69. Time for learning and reflection of two weeks or more. 0.883 70. Being allowed to participate as a school team.
0.709 71. Follow-up visits and assistance by a site coordinator.
Section G, Practical Benefits of the survey asked participants to describe their agreement concerning the influence of benefits offered as incentives for participation in professional development activities on their choice of and participation in such programs. Section G was
subjected to data reduction via principal component analysis using varimax rotation. The six items loaded under 2 components. Five of the six items loaded highest under component 1. Item 70 loaded highest under component 2. The components were renamed PRACBEN, for practical benefits and GRADTEAM for participation in grade level teams. The components were saved and used in regression analysis.
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Results and Analyses of Data to Answer Research Questions
Several steps were taken to analyze data and answer the five research questions. Each of the research questions is focused on factors that influence LaSIP teachers’ selection and implementation of changes in teaching practices following participation in formal professional development activities. The first step was to compute and analyze descriptive statistics for each of the sections of the survey A-G. The raw data for both LaSIP and Non-LaSIP participants were recorded in SPSS data tables.
Recoding
.
Recoding is a feature available in statistical software such as SPSS that can be used to modify a data set by collapsing a larger number of categories into a smaller set. Instead of using a 5 category Agree-Disagree scale for the Likert items, I simplified the scale to three categories, Agree,Not sure and Disagree. This strategy simplified interpretation of the data and made reporting of the findings from frequency distributions easier without loss of information. The original data set was saved in a separate file in case it was needed for later analysis or verification.The five -point Likert scales for Sections A-D and G were recoded to read, Agree = 3; Not Sure =2 and Disagree = 1. The subscale for Section E, Implementation of Research-based Teaching Strategies was recoded to read: three or more times weekly = 3; two times weekly = 2 and once weekly or less =1. The subscale for Section E. Effect of Research-based Strategies on Student Achievement was recoded as follows: Increased = 3; Remained the same = 2 and
Decreased = 1. Descriptive statistics, frequency distributions and data from factor and
regression analyses were generated using the recoded scales. The results were recorded in tables, analyzed and interpreted to answer the following research questions:
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Research Question 1. Which reform-based program components are perceived by LaSIP science teachers as being most important in improving their professional growth and are most likely to influence their selection and implementation of research-based teaching strategies in the classroom?
Table 11 is a summary of frequency distributions for Section A, Features of the LaSIP. Items 1-11 of the survey were designed to ascertain teacher perceptions of features of LaSIP professional development programs that were most influential in improving teaching and learning and contributed to implementation of the training experiences in the classroom.
Table 11.
Summary of Frequency Distributions Section A. Features of the LASIP
Features of the LaSIP professional development experiences that were most influential in improving teaching and learning and contributing to my use of the training experiences in the classroom included:
Teacher Response in Percents 3 Agree 2 Not Sure 1 Disagree 1. Sufficient time for acquiring the pedagogical
content knowledge to implement the concepts and strategies in a classroom setting.
80 5 15
2. Emphasis on standards-based teaching and learning. 90 2 8 3. Time for reflection and writing about teaching and
learning experiences.
72 10 18
4. Activities that emphasized the use of science process skills.
72 13 15
5. Instruction in alternative assessment that included
models of authentic, real-life experiences. 82 10 8 6. Modeling teaching and learning strategies during
microteaching activities.
74 11 15
7. Emphasis on learning major science concepts. 69 10 21 8. Time to practice research-based teaching strategies. 69 10 21 9. Opportunities to learn through a variety of methods 75 10 15 10. Attention to learning styles and multiple
intelligences that were useful in classroom instruction.
82 5 13
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There was widespread agreement among respondents that features of the LaSIP as described in Section A contributed to improvement of teaching and learning and influenced
implementation of the training into classroom practice. The item that showed the greatest agreement among LaSIP teachers was Item Number 2, program emphasis on standards-based teaching and learning. Overall, an average of 75% of teachers agreed with the statements in Section A of the survey, 10% of teachers were not sure and 15% of teachers disagreed with the statements.
Based on the theoretical model shown in Figure 4, another part of question 1 to be
answered is, which of the professional development program features in Section A are predictive of levels of implementation of research-based teaching strategies (RBTS) described in Section E of the survey? Sections A and E were subjected to regression analysis in order to answer this question. Results of principal component analysis of Section A are shown in Table 4. The three components were used as independent variables in regression analysis. Results of principal component analysis of Section E are shown in Table 8. The factor RBTS was used as the dependent variable subjected to regression analysis.
The items in Section A of the survey pertain to specific features of the LaSIP. Therefore, it seems feasible that a regression model that includes a majority of the features as independent variables or predictors could provide more detailed information than use of the composite variables shown in Table 4. Using a stepwise approach, each of the 11 items was used in regression analysis models. Results of the most inclusive model are summarized in Table 12. Table 12 is a summary of the results of linear regression analysis of variables in Section A, Features of the LaSIP, as predictors for implementation of research-based teaching strategies (RBTS) in Section E. The model summary table (12 A.) shows that the multiple correlation
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coefficient (R) using all the 9 predictors simultaneously, is 0.98 (R2 = 0.97) and the adjusted R2
is 0.96 meaning that 96% of the variance in LaSIP teachers’ perceptions of frequency of implementation of research based teaching strategies following participation in LaSIP can be predicted from questions 1, 2, 5, 6, 7, 8* 9, 10 and 11 combined, with 8 of the 9 variables
significantly contributing to the prediction. The adjusted R2 value of 0.96 means that 96% of the variance in implementation was explained by the model. According to Leech, Barrett & Morgan ((2008), this is a large effect. The findings were significant at p < 0.001 and therefore supportive