Available online at www.sciencedirect.com
ScienceDirect Procedia - Social and Behavioral Sciences 217 (2016) 638 – 642
Future Academy®’s Multidisciplinary Conference
The State of Teachers’ Educational Data Use in Thailand Kulratee Phanchalaema, Siridej Sujivaa*, Kamonwan Tangdhanakanonda a
Department of Educational Research and Psychology, Faculty of Education, Chulalongkorn University, Bangkok 10330, Thailand
Abstract Teachers’ educational data use is important as it helps students’ learning development directionally. The objectives of this study were to study (1) educational data use existing in schools; (2) state of teachers’ educational data use and (3) teachers’ needs in educational data use. Samples consisted of 250 elementary school teachers in Thailand using sequential mixed method design. Data were analyzed using content analysis and descriptive statistics. It was found that there are 11 types of educational data use in Thai schools. The educational data existing in most schools was teachers’ teaching data, followed by students’ learning proficiency data and students’ academic achievement data, respectively. Teachers also used such data to improve and develop students as much as possible, whereas physical or behavioral flaw and life skill data were rarely found and infrequently used. Data analysis was most needs by teachers to develop, followed by application of statistics in data analysis and interpretation of data analysis, respectively. Data collection for planning teaching and improving students and cooperation with colleague in data use were most needed by teachers to use data in teaching and improving learners, followed by application of data in setting strategies for teaching and improving student learning and interpretation of numerical or statement forms from evidence documents, respectively. © by Elsevier Ltd. by This is an open © 2016 2016Published The Authors. Published Elsevier Ltd.access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Future Academy® Cognitive Trading. Peer-review under responsibility of Future Academy® Cognitive Trading Keywords:Eucational Data; Data Use; Thailand
* Corresponding author. Tel.: +6686 896 3220 E-mail address:
[email protected].
1877-0428 © 2016 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Future Academy® Cognitive Trading doi:10.1016/j.sbspro.2016.02.084
Kulratee Phanchalaem et al. / Procedia - Social and Behavioral Sciences 217 (2016) 638 – 642
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1. Introduction Data has been used, both domestically and internationally, in educational system for a long time. Foreign countries began giving importance to educational data when the United States issued “No Child Left Behind Act” (NCLB) in 2001 with the intention of data use consideration increase in every schools and educational service area (Massell, 2001). In the meantime, Thailand stipulates teachers to use data obtained from students’ learning assessment in academic curriculum. Therefore, data use is nothing new to the existing educational system. New data occurs daily. Hence, there is pressure on educators to use more data. Therefore, effective data use requires more than numerical or statistical properties in order to ensure the meaningfulness and values for the teachers’ teaching improvement. However, previous studies showed that the data use was still low. For example, Hamilton and colleagues (2009) discovered that teachers collect large volumes of educational data but not systematically. Moreover, meaningful application toward students’ learning improvement is still deficient. Previous findings concluded that an important obstacle causing teachers to neglect data is usage incapacity (Greenberg & Walsh, 2012; Marsh et al., 2005). The researcher realizes the aforementioned problem. Furthermore, few studies on educational data use have been conducted in Thailand. Therefore, the present research studied the state and the need of data use of teachers in Thailand to obtain beneficial findings in capacity development planning in educational data use correctly and properly. 2. Research Objectives The objectives of this study were to study (1) educational data use existing in schools; (2) state of teachers’ educational data use and (3) teachers’ needs in educational data use. 3. Literature Review 3.1 Type of data Mandinach (2012) defined educational data use that it involves systematic data collection, data analysis, data examination and data interpretation from diverse data for making decisions to improve performance and educational policies. From school system considerations, a large amount of data was found. Bernhardt (2004) categorized data into four following types: 1) Student Learning Performance - it is deemed the most important aspect in the education system; 2) Demographics - the use of this type of data aids the clarification of problems and requirements associated with students, e.g., gender, race, economic status etc.; 3) School Process – Most of the data sources are the qualities of various teaching programs in schools and 4) Perception – it is associated with community opinions towards schools. These data stimulate students’ interest towards community opinions and thinking. 3.2 Data Literacy The effective use of currently available data demands that school principals, teachers and districts have knowledge and skills in the use of data. In other words, they need assessment literacy and data literacy in order to reach effective teaching decision-making (Love, 2004). According to literature review, neither terms differ with clarity. Assessment understanding is an essential component for decision-making through data use (Heritage & Yeagley, 2005; Herman & Gribbons, 2001). Data from assessments, collected from classrooms, schools or districts measuring academic proficiency is only a form of data that enters the decision-making processes in data literacy, since other forms of literacy obtained from assessments of other data sources are present, e.g., perception, motivation, processes and behaviors. Therefore, assessment literacy is considered as a process of data literacy (Mandinach & Gummer, 2013).
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4. Methodology The study was based on a sequential mixed method design as shown in figure 1. It was divided into two stages: In stage 1, developing the Research Instrument– qualitative data was collected from qualitative data collection from evidence documents and teacher interview on educational data existing in schools to answer the first research objective. The interview samples consisted of 6 teachers in two purposively selected schools (one small school and one large school). The research instrument was an informal interview form. Data was analyzed through content analysis. As for stage 2, conducting the survey study quantitative data on state and needs of data use were collected. The samples consisted of 250 teachers (190 females and 60 males) from every regions of Thailand in which 152 had graduated bachelor degrees and 98 had graduated master degrees. Up to 167 teachers had less than 20 years of working experience, while 83 teachers had over 20 years of working experience. The study was conducted using multi-stage random. The instrument used was a teacher questionnaire on the state of teachers’ data use and teachers’ needs in data use consisting of checklist and five-rating scale items. Data analysis was conducted through descriptive statistics: i.e., frequency, percentage, and mean point. Modified Priority Need Index (PNImod ) was used to arrange the teachers’ needs in educational data use in 2 aspects, i.e., (1) knowledge and skill in data use and (2) using data in teaching and improving learners. stage 1
QUAL data collection
stage 2
QUAL data analysis
QUAL results
construct questions for survey
QUAN data collection
QUAN data analysis
QUAN results
Fig.1. The exploratory design-instrument development model (Creswell, J.W. and Plano Clark, V.L, 2007)
5. Findings 5.1 Data use existing in schools There are 11 types of educational data in Thai schools: 1) students’ background data, 2) physical or behavioral flaw data, 3) students’ basic ability data, 4) students’ family data, 5) students’ learning proficiency data, 6) student’s behavior data, 7) students’ life skill data, 8) students’ academic achievement data, 9) school data, 10) teachers’ teaching data and 11) parents and communities’ school-awareness perception data 5.2 State of teachers’ educational data use According to the 11 types of data in 5.1, the data existing in most schools was teachers’ teaching data, followed by students’ learning proficiency data and students’ academic achievement data, respectively. Teachers also used such data to improve and develop students as much as possible, whereas physical or behavioral flaws data and life skill data rarely found and infrequently used. The majority of teachers commented that students’ learning proficiency data, students’ academic achievement data and teachers’ teaching data were useful for teaching and learning development at high level.
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5.3 Teachers’ needs in using data In knowledge and skills in educational data use aspect, teachers had most needs in data analysis (PNImod = 0.288), followed by application of statistics in data analysis (PNImod = 0.288), interpretation of data analysis (PNImod = 0.277) and use of computer technology in the analysis and storage of data (PNImod = 0.262), respectively. Table 1. The priority need index (PNImod) for using teachers’ educational data. expected (I)
What is (D)
PNI = I-D/D
rank
1. Data analysis
Knowledge and skills in the use of data
4.432
3.440
0.288
1
2. Application of statistics in data analysis
4.416
3.428
0.288
1
3. Interpretation of data analysis
4.424
3.464
0.277
2
4.416
3.500
0.262
3
4. Use of computer technology in the analysis and storage of data
In using data in teaching and improving learners aspect, teachers had most needs in data collection for planning teaching and improving students (PNImod = 0.255), followed by cooperation with colleague in data use (PNImod = 0.225), application of data in setting strategies for teaching and improving student learning (PNImod = 0.215) and interpretation of numerical or statement forms from evidence documents (PNImod = 0.209), respectively. Table 2. The priority need index (PNImod) for using teachers’ educational data. Using data in teaching and improving learners expected (I) 1. Data collection for planning teaching and improving 4.204 students 2. Application of data in setting strategies for teaching and 4.380 improving student learning 3. Cooperation with colleague in data use 4.360 4. Interpretation of numerical or statement forms from 4.312 evidence documents
What is (D)
PNI = I-D/D
rank
3.432
0.225
1
3.604
0.215
2
3.56
0.225
1
3.568
0.209
3
6. Conclusion & Discussion There were 11 types of educational data in Thai schools. The data existing in most schools was teachers’ teaching data, students’ learning proficiency data and students’ academic achievement data. Teachers also used such data to improve and develop students as much as possible. This finding were consistent with a study conducted by Bernhardt (2004) in which the researcher classified the aforementioned these data as student learning data and had stated that the type of student learning data is the most important aspect of the education system. Teachers had most needs in data analysis to develop knowledge and skill in data use. Moreover, teachers had most needs in data collection for planning teaching and improving students was needed the most by teachers to use for teaching and improving learners, followed by cooperation with colleague in data use. The finding were also consistent with a study done by Greenberg and Walsh (2012) on research conducted in the past ten years revealed that key weaknesses of teachers were the data analysis and the inability to apply data obtained from student assessment in decision-making. References Bernhardt, V. L. (2004). Using data to improve student learning in middle schools. New York, NY: Routledge. Creswell, J. W. & Plano Clark, V. L. (2007). Designing and conducting mixed methods research. Cohen Research on Social Work. Thousand Oaks, CA: Sage. Greenberg, J. & Walsh, K. 2012. What teacher preparation programs teach about K-12 assessment: A review. [Online]. Retrieved from: http://www.nctq.org/dmsView/What_Teacher_Prep_Programs_Teach_K-12_Assessment_NCTQ_Report. [2013, September 3]
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Kulratee Phanchalaem et al. / Procedia - Social and Behavioral Sciences 217 (2016) 638 – 642 Hamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Heritage, M., & Yeagley, R. (2005) Data use and school improvement: Challenges and prospects. Yearbook of the National Society for the Study of Education, 104(2), 320–339. Herman, J. L., & Gribbons, B. 2001. Lessons learned in using data to support school inquiry and continuous improvement: Final report to the start foundation. Center for the Study of Evaluation (CSE) University of California, Los Angeles. Love, N. 2004. Taking data to new depths. Journal of Staff Development. 25)4(, 22–26. Mandinach, E. B. 2012. A perfect time for data use: Using data-driven decision making to inform practice. Educational Psychologist. 47 2 71-85. Mandinach, E. B., & Gummer, E. S. 2013. A Systemic view of implementing data literacy in educator preparation. Educational Researcher, 42 30, 30-37. Marsh, A. J., Kerr, A. K., Ikemoto, S.G., Darilek, H., Suttorp, M., Zimmer W. R., & Barney H. (2005).The role of districts in fostering instructional improvement: Lessons from three urban districts partnered with the institute for learning, Santa Monica, CA: RAND Corporation. Massell, D. (2001). The theory and practice of using data to build capacity: State and local strategies and their effects. In S. H. Fuhrman (Ed.), From the capitol to the classroom: Standards-based reform in the states. Chicago: University of Chicago Press.
