Vol. 2 Iss. 1

STEM Education and Research in a Changing World: Our Social Responsibility

Lucy Avraamidou

Download: FULL TEXT PDF
Download: 148, size: 0, date: 06.Jan.2020

At the awaking of the third millennium, in the here and now, the world finds itself facing a series of challenges, such as climate change, poverty, inequality, refugee crisis, unemployment, and so on, and so on. These global challenges raise a number of questions for STEM education and research: What should we teach our children? What knowledge and skills will our children need to have in 2050? How can we utilize scientific and technological knowledge to address global challenges? How can we think beyond the here and now in order to prepare ourselves for the future societies? Essentially, two questions are raised for STEM researchers: (a) what is the role of STEM education and research in a constantly changing world? and, (b) How does STEM shape our societies and how are our societies shaped by STEM?

Vol. 2 Iss. 1

Reliability of ACCUPLACER Score in Predicting Success in Quantitative Reasoning Course

Santhosh Mathew, & Upasana Kashyap

Download: FULL TEXT PDF
Download: 226, size: 0, date: 06.Jan.2020

Abstract: The purpose of this study was to determine the correlation between the ACCUPLACER placement test score (elementary algebra) and the student success in the quantitative reasoning course at Regis College. Our study points to a weak but significant correlation between the ACCUPLACER placement score and the student success in the quantitative reasoning course. We propose that an in-house placement system based on the unique requirements of the institution will be a much more effective approach to place the students at appropriate levels of instruction.

Keywords: ACCUPLACER; College Placement; Quantitative Reasoning; Freshmen Level Mathematics; Assessment of Student Preparedness

Please Cite: Mathew, S., & Kashyap, U. (2019). Reliability of ACCUPLACER score in predicting success in Quantitative Reasoning Course. Journal of Research in Science, Mathematics and Technology Education, 2(1), 1-7. DOI: https://doi.org/10.31756/jrsmte.211             

References

Anthony, G. (2000). Factors influencing first-year students' success in mathematics. International Journal of Mathematical Education in Science and Technology 31(1), 3–14.

Armstrong, W. B. (2000). The association among student success in courses, placement test scores, student background data, and instructor grading practices. Community College Journal of Research & Practice, 24 (8), 681– 695.

Belfield, C. R., & Crosta, P. M. (2012). Predicting success in college: The importance of placement tests and high school transcripts (CCRC Working Paper No. 42). New York: Community College Research Center.

Bennett, J., & Briggs W.L. (2014). Using and understanding mathematics: A   Quantitative Reasoning Approach. 6th ed., MA: Pearson.

Bettinger, E. P., & Long, B. (2009). Addressing the needs of under-prepared students in Higher Education: Does college remediation work? Journal of Human Resources, 44(3), 736–771. 

Brase, C. H. & Brase, C.P. (2012). Understandable Statistics: Concepts and Methods (10th edn). Boston: Brooks/Cole Cengage Learning.

College Board. (2015, March 12). ACCUPLACER. Retrieved from

              http://professionals.collegeboard.com/higher- ed/placement/ACCUPLACER.

Elrod, S.L. (2014). Quantitative Reasoning: The Next “Across the Curriculum” Movement, Peer Review 16(3), 4–8.

Greene, J.P. & Forster, G. (2003, September). Public High School Graduation and College Readiness Rates in the United States, (Manhattan Institute, Center for Civic Information, Education Working Paper, No. 3). New York: Manhattan Institute.

Madison, B. L., Linde, C. S., Decker, B. R., Rigsby, E. M., Dingman, S. W., & Stegman,   C. E. (2015). A Study of Placement and Grade Prediction in First College Mathematics Courses. PRIMUS, 25(2), 131-157.

ACCUPLACER Reliability & Validity (2015, May 20). Retrieved from https://accuplacer.collegeboard.org/sites/default/files/accuplacer-realiability-validity.pdf

Mattern, K. D., & Packman, S. (2009). Predictive validity of ACCUPLACER scores for course placement: A meta-analysis (Research Report No. 2009-2). New York, NY: College Board. Retrieved from https://research.co.llegeboard.org/sites/default/files/publications/2012/7/researchreport-2009-2-predictive-validity-ACCUPLACER-scores-course-placement.pdf

Ngo, F., & Kwon, W. W. (2015). Using multiple measures to make math placement decisions: Implications for access and success in community colleges. Research in Higher Education, 56(5), 442-470.

Rueda, N. G., & Sokolowski, C. (2004). Mathematics placement test: Helping students succeed. The Mathematics Educator, 14(2), 27-33.

Scott-Clayton, J. (2012). Do high-stakes placement exams predict college success? (CCRC Working Paper No. 41). New York, NY: Columbia University, Teachers College, Community College Research Center.

Sedlacek, W. E. (2004). Beyond the big test: Noncognitive assessment in higher education. San Francisco: Jossey-Bass.

Steen, L. (2004). Achieving Quantitative Literacy: an Urgent Challenge for Higher Education. MAA Notes. Washington, DC: Mathematical Association of America.

  

Vol. 2 Iss. 1

Framework for the Parallelized Development of Estimation Tasks for Length, Area, Capacity, and Volume in Primary School – A Pilot Study

Dana Farina Weiher

Download: FULL TEXT PDF
Download: 150, size: 0, date: 06.Jan.2020

Abstract: The purpose of this study is to present a framework for the development of parallelized estimation tasks for the visible measures length, area, capacity, and volume. To investigate if there are differences between the estimation types of task, a written estimation test for 3rd- and 4th-graders was developed. It includes eight different types of task for each measure. The percentage deviation of the estimated value from the real value (the measured size) of 137 students indicates that there are differences between the four measures as well as within the types of task that affect over- and underestimation and the estimation accuracy. Further research could address relations between the estimation of visible measures and the investigation of more characteristics in an estimation task, using a written estimation test that is based on this valid framework.

Keywords: Estimation test; Estimation tasks; Measurement estimation; Parallelized items; Visible measures

Please Cite: Weiher, D. F. (2019). Framework for the Parallelized Development of Estimation Tasks for Length, Area, Capacity, and Volume in Primary School – A Pilot Study. Journal of Research in Science, Mathematics and Technology Education, 2(1), 9-28. DOI: https://doi.org/10.31756/jrsmte.212     

References

Bright, G. W. (1976). Estimation as Part of Learning to Measure. In D. Nelson (Ed.), Measurement in School. Mathematics 1976 Yearbook (pp. 87–104). Reston, VA: National Council of Teachers of Mathematics.

Brand, M., Fujiwara, E., Kalbe, E., Steingass, H.-P., Kessler, J., & Markowitsch, H. J. (2003). Cognitive Estimation and Affective Judgments in Alcoholic Korsakoff Patients. Journal of Clinical and Experimental Neuropsychology, 25 (3), 324-334. doi: http://dx.doi.org/10.1076/jcen.25.3.324.13802

D’Aniello, G. E., Castelnuovo, G., & Scarpina, F. (2015). Could Cognitive Estimation Ability Be a Measure of Cognitive Reserve? Frontiers in Psychology, 6, 1-4. doi: http://dx.doi.org/10.3389/fpsyg.2015.00608

Heinze, A., Weiher, D. F., Huang, H.-M., & Ruwisch, S. (2018). Which Estimation Situations are Relevant for a Valid Assessment of Measurement Estimation Skills? Proceedings of the 42nd Conference of the International Group for the Psychology of Mathematics Education (Vol. 1). Umeå, Sweden: PME.

Hildreth, D. J. (1983). The Use of Strategies in Estimating Measurements. Arithmetic Teacher, 30 (5), 50-54.

Hogan, T. P., & Brezinski, K. L. (2003). Quantitative Estimation: One, Two, or Three Abilities? Mathematical Thinking and Learning, 5 (4), 259-280. doi: http://dx.doi.org/10.1207/S15327833MTL0504_02

Joram, E. (2003). Benchmarks as Tools for Developing Measurement Sense. In D. H. Clements & G. Bright (Eds.), Learning and Teaching Measurement. 2003 Yearbook (pp. 57-67). Reston, VA: National Council of Teachers of Mathematics.

Joram, E., Subrahmanyam, K., & Gelman, R. (1998). Measurement Estimation: Learning to Map the Route from Number to Quantity and Back. Review of Educational Research, 68(4), 413-449. doi: http://dx.doi.org/10.3102/00346543068004413

MacPherson, S. E., Wagner, G. P., Murphy, P., Bozzali, M., Cipolotti, L., & Shallice, T. (2014). Bringing the Cognitive Estimation Task into the 21st Century: Normative Data on Two New Parallel Forms. PLoS ONE 9(3): e92554. doi: http://dx.doi.org/10.1371/journal.pone.0092554

Nührenbörger, M. (2004). Children’s Measurement Thinking in the Context of Length. In G. Törner, R. Bruder, A. Peter-Koop, N. Neill, H. G. Weigand, & B. Wollring (Eds.) Developments in Mathematics Educations in German-speaking Countries. Selected Papers from the Annual Conference on Didactics of Mathematics. Ludwigsburg 2001. (pp. 95-106).

O’Daffer, P. (1979). A Case and Techniques for Estimation: Estimation Experiences in Elementary School Mathematics – Essential, Not Extra!. The Arithmetic Teacher, 26 (6), 46-51.

Shapiro, S. S., & Wilk, M. B. (1965). An Analysis of Variance Test for Normality (Complete Sample). Biometrika, 52 (3/4), 591-611. doi: http://dx.doi.org/10.2307/2333709

Siegel, A. W., Goldsmith, L. T., & Madson, C. R. (1982). Skill in Estimation Problems of Extent and Numerosity. Journal for Research in Mathematics Education, 13 (3), 211–232. doi: http://dx.doi.org/10.2307/748557

Winter, H. (2003). Sachrechnen in der Grundschule. Problematik des Sachrechnens. Funktionen des Sachrechnens. Unterrichtsprojekte. (6th ed.). Frankfurt am Main: Cornelsen Scriptor.

          

Vol. 2 Iss. 1

Using e-learning in pre-service English teacher education in Chinese fourth-tier cities: An exploratory study

Ruiqian Yang & Yiu Chi Lai

Download: FULL TEXT PDF
Download: 180, size: 0, date: 06.Jan.2020

Abstract: Nowadays, e-learning is widely adopted in all education sectors, but different teachers utilize different strategies to teach their students. In the field of teacher education, views on the ways in which e-learning can be used are also varied. It is worth exploring how to implement e-learning in courses and how student teachers can apply the e-learning strategies they learned during their own field experience courses. A better understanding of the current practice will not only help teacher educators and student teachers to understand relevant pedagogical approaches in regard to e-learning, but will also enable student teachers to learn how to use appropriate e-learning strategies in their classes. This study explores the e-learning strategies adopted in teacher education courses for pre-service English teachers in mainland China, with a focus on Chinese fourth-tier cities. A total of 475 student teachers were involved and a mixed-method research approach was adopted. Both qualitative and quantitative data were collected via questionnaires and interviews. The findings can enhance the current understanding of the common strategies used in e-learning in English pre-service teacher education courses in Chinese fourth-tier cities. We also give some suggestions for better future e-learning pedagogical approaches.

Keywords: Chinese fourth-tier cities; e-learning; Pre-service teacher education

Please Cite: Yang, R. & Lai, Y., C. (2019). Using e-learning in pre-service English teacher education in Chinese fourth-tier cities: An exploratory study. Journal of Research in Science, Mathematics and Technology Education, 2(1), 29-57. DOI: https://doi.org/10.31756/jrsmte.213           

References

Baker, W., & Watson, J. (2014). Mastering the online Master’s: Developing and delivering an online MA in English language teaching through a dialogic-based framework. Innovations in Education and Teaching International, 51(5), 483-496.
  1. Sun. (2017). The-strategy-inventory-of-language-learning-sill. Retrieved from https://languagelearning.stackexchange.com/questions/3053/what-is-the-strategy-inventory-of-language-learning-sill.
Cai, H. (2012). E-learning and English teaching. IERI Procedia, 2, 841-846.

Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education (6th ed.). London: Routledge/Falmer.

Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). Advanced mixed methods research designs. Handbook of mixed methods in social and behavioral research, 209, 240.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Fee, K. (2009). Delivering e-;earning: A complete strategy for design application and assessment (1st ed.). London and Philadelphia, PA: Kogan Page Publishers.

Gao, L. (2012). Digital technologies and English instruction in China’s higher education system. Teacher Development, 16(2), 161-179.

Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14-26.

Krashen, S. D. (1982). Principles and practice in second language acquisition. Oxford: Pergamon Press.

Laferrière, T., Lamon, M., & Chan, C. (2006). Emerging e-trends and models in teacher education and professional development. Teaching Education, 17(1), 75-90.

Lotfi, A. R., & Joybar, B. (2015). Theoretical frameworks of language development: A library research. Modern Journal of Language Teaching Methods, 5(4), 420.

Macaro, E. (2002). Learning strategies in foreign and second language classrooms: The role of learner strategies. London: Bloomsbury Publishing.

Newson, J. (1999). Techno-pedagogy and disappearing context. Academe, 85(5), 52.

Oxford, R. (1990). Language learning strategies: What every teacher should know (1st ed.). Boston, MA: Heinle and Heinle.

Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544.

Salmon, G. (2011). E-moderating: The key to teaching and learning online (3rd ed.). London: Routledge/Falmer.

Sun, Q (2017, May 31). Xin Yi Xian Cheng Shi Pai Hang Bang Fa Bu Chengdu Huangzhou Wuhan Chan Lian San Jia Zhengzhou Dongguan Xin Jin Ru Bang. First Finance.

Tan, P. (2015). English e-learning in the virtual classroom and the factors that influence ESL (English as a Second Language): Taiwanese citizens’ acceptance and use of the Modular Object-Oriented Dynamic Learning Environment. Social Science Information, 54(2), 211-228.

World Bank. (2011). Learning for all: Investing in people’s knowledge and skills to promote development – World Bank Group Education Strategy 2020: Executive Summary (English). Washington, DC: World Bank.

    

Vol. 2 Iss. 1