data analysis techniques

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Participatory observations Free from bias Derived from student questionnaire Response frequencies in data Compiling Disassembling Reassembling Interpreting Drawing conclusions DATA ANALYSIS TECHNIQUES

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Data Analysis Techniques. Participatory observations Free from bias Derived from student questionnaire Response frequencies in data Compiling Disassembling Reassembling Interpreting Drawing conclusions. Data Analysis Techniques. Refection Action Plan Implementation - PowerPoint PPT Presentation

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Page 1: Data Analysis Techniques

Participatory observationsFree from biasDerived from student questionnaireResponse frequencies in data

CompilingDisassemblingReassembling InterpretingDrawing conclusions

DATA ANALYSIS TECHNIQUES

Page 2: Data Analysis Techniques

Refection Action PlanImplementationModification of methodologyTriangulation of data

DATA ANALYSIS TECHNIQUES

Page 3: Data Analysis Techniques

ConsentAssentVoluntaryAnonymity

RESEARCH ETHICS

Page 4: Data Analysis Techniques

Reasons for Students not Studying Outside of Class Jobs Athletics Apathy Self-confidence Non-interest in subject matter Family commitments

Students are indeed predisposed to lack motivation in school for a variety of reasons. (Ford, 2002) If students feel they are not capable of success, they will not

perform. Belief in one’s ability and one’s effort are equally

necessary antecedents to successful academic student achievement. (Ford, 2002)

ANALYSIS AND FINDINGS

Page 5: Data Analysis Techniques

Hindered due to sleep?Lack of motivation or desire?Are they not prepared?When the task is not an integral component of a

student’s life, or if, in effect, it is not important to the student, amotivation may result. (LeGault, Pelletier, Green-Demeers, 2006).

Content needs to be relevant. The classroom content should strive to foster students’ intrinsic motivation. (Ford, 2002).

Instructors should try and plan activities based on relevant topics. (Ford, 2002).

FACTORS PREVENTING STUDENT ENGAGEMENT

Page 6: Data Analysis Techniques

Revise the format of the classImplement student led modules based on NGSS, KCC

and WKU StandardsFlip to a more digital format (Google Docs)Increased rigor

GENERAL CONCERNS WITH THE FORMAT OF THE CLASS

Page 7: Data Analysis Techniques

Initial Study

Why decline in motivation?What prevents engagement?Two years of data showed

ninth grade outperform tenth grade

Ninth grade study more than tenth grade

Extension Study

Does block lend itself academic success?

Two years of data do not show an overall increase

Eleventh grade students are not outperforming ninth grade students

More research is needed

CONCLUSION

Page 8: Data Analysis Techniques

Improved teaching strategies in classroom KTS and ISTEDirect impact KTS standard 7

Strands 7.1 and 7.2

RATIONALES

Page 9: Data Analysis Techniques

TimeDifferent schools / different instructorPerspective

LIMITATIONS

Page 10: Data Analysis Techniques

PLCValidity?Third study

RECOMMENDATIONS FOR FUTURE STUDY

Page 11: Data Analysis Techniques

Busteed, C. and Bergin, D. (2009). Attachment in the classroom. Educational Psychological

Review.21, 141-170. Retrieved from http://edusource.org/wp- content/uploads/ClassroomAttachment.pdf

Bynoe, Tyrone. (2014). ADOL 633 Course content KTS and ISTE standards. Retrieved from ttps://ucumberlands.blackboard.com/webapps/portal/frameset.jsp?tab_tab _group_id=_2_1&url=%2Fwebapps%2Fblackboard%2Fexecute %2Flauncher%3 Ftype%3DCourse%26id%3D_46712_1%26u

SOURCES

Page 12: Data Analysis Techniques

Ford, Valjeaner. (2002). Why do high school students lack motivation? Global Education Journal. 101-113.

Retrieved from http://libres.uncg.edu/ir/uncp/f/Why%20Do%20High%2 0S chool%20Students%20Lack%20Motivation%20in%20the %20Classroom.pdf

RESOURCES

Page 13: Data Analysis Techniques

Irmsher, Karen. (1996). Block Scheduling. ERIC Digest, Number 104. Retrieved fromhttp://files.eric.ed.gov/fulltext/ED393156.pdf

Lawrence, W. & McPherson, D. (2000). A comparative study of block scheduling and

traditional scheduling on academic achievement. Retrieved from http://curriculum.austinisd.org/soc_stud/resources/doc uments/ComparativeStudyofBlockvsTraditional.pdf

SOURCES

Page 14: Data Analysis Techniques

Legault, L., Pelletier, L. & Green-Demers, I. (2006). Why do high school students lack motivation in the classroom? Toward the understanding of amotivation and the role of social support. Journal of Educational Psychology. 98 (3) 567-582. American Psychological Association. Retrieved from http://selfdeterminationtheory.org/SDT/documents/200 6_LegaultGreenPelletier_JEP.pdf

Musbach, Jennifer. ( 2006). Saline area school district and Ypsilanti public schools. University

of Michigan. Retrieved from http://sitemaker.umich.edu/musbach.356/traditional_vs._block_schedule_

SOURCES

Page 15: Data Analysis Techniques

Parsons, J. and Taylor, L. (2011). Student engagement: what do we know and what should we

do? AISI School Improvement Press. Retrieved from http://education.alberta.ca/media/6459431/student_engagement_literature_review_2011.pdf

Rettig, Michael. (1999). The effects of block scheduling. Two leading authorities describe

what results when high schools use alternative schedules. The School Administrator. Retrieved from http://www.aasa.org/SchoolAdministratorArticle.aspx?id =14852

SOURCES

Page 16: Data Analysis Techniques

Taylor, L. & Parsons, J. (2011). Improving student engagement. Current Issues in

Education.14(1).http://cie.asu.edu/ojs/index.php/cieata su/article/view/745/162

Wilson, J. T. (2014). Students’ perspective on intrinsic motivation to learn: a model to guide educators. A Journal of the International Christian Community for teacher Education, 9 (1). Retrieved fromhttp://icctejournal.org/issues/v6i1/v6i1-wilson/

Wilson, J. T. (2014). Students’ perspective on intrinsic motivation to learn: a model to guide educators. A Journal of the International Christian Community for teacher Education, 9 (1). Retrieved fromhttp://icctejournal.org/issues/v6i1/v6i1-wilson/

 

SOURCES