Career and Technical Educators Using a Data Driven Improvement Model Collection of data: NOCTI pretest scores Fall semester grades Fall semester attendance.

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Presentation transcript:

Career and Technical Educators Using a Data Driven Improvement Model Collection of data: NOCTI pretest scores Fall semester grades Fall semester attendance Fall semester LASSI results 1

Career and Technical Educators Using a Data Driven Improvement Model NOCTI Pretest scores: 16 students scored above the national average in 7 areas, below the national average in one area. (5 areas without national average data) In the area scoring below the national average, 10 students scored below the national average. 2

Career and Technical Educators Using a Data Driven Improvement Model Fall semester grades: In 5 courses, 83.8% of students who completed the courses (attended for the full semester) earned a grade of a C or higher. (My personal goal had been 91%) 3

Career and Technical Educators Using a Data Driven Improvement Model Fall semester attendance: 137 students enrolled in 5 courses 2 on campus courses, 3 online courses (each course met for 8 weeks-an experiment) 18 withdrew 58 never attended (42%!!) 4

Career and Technical Educators Using a Data Driven Improvement Model LASSI test includes 10 areas- Anxiety Attitude Concentration Information Motivation Self-testing Selecting idea Study aids Time management Test strategy 5

Career and Technical Educators Using a Data Driven Improvement Model The LASSI pretest was given on 9/6/11 First time, 8 week course time frame The two lowest scoring areas were motivation and attitude Intervention/lecture given 9/12/11 Post test given 10/13/11 68% showed improvement in or or more areas 6

Career and Technical Educators Using a Data Driven Improvement Model Analyzing the data: NOCTI scores-area scoring below the national average was child development-cognitive development Another area scoring above the national average but second lowest in average of the group-diversity in the classroom 7

Career and Technical Educators Using a Data Driven Improvement Model Final grades: More than the majority of students who completed the courses achieved a grade of a C or higher. 8

Career and Technical Educators Using a Data Driven Improvement Model LASSI results: A high percentage of students lacked motivation and/or displayed a lack of attitude towards attending. When the same group retested one month later (after a discussion on time management, attitude, employment, passion for what they want to do) both areas-motivation and attitude dramatically increased. 9

Career and Technical Educators Using a Data Driven Improvement Model Action plan: Identify MN Core competencies that match with lower NOCTI scores and then which child development courses include those components. Review instructional delivery, assignments, and course assessments-demonstrations, quiz, tests, etc. for these components/competencies areas. 10

Career and Technical Educators Using a Data Driven Improvement Model 11

Career and Technical Educators Using a Data Driven Improvement Model Triangulate data to identify relationship of attendance, LASSI results, semester grade and NOCTI score. Schedule department meeting to discuss changes, if needed, to delivery, emphasis of concept/competency, and assessment competencies. Determine priorities for addressing LASSI results. 12

Career and Technical Educators Using a Data Driven Improvement Model Action plan: First week of instruction-does the student really belong in the class. Many (42%) may not have known what they were doing when they registered because they didn’t show up or withdrew at some point. Plan discussion of time management, attitude, motivation, etc. in first week of the course. 13

Career and Technical Educators Using a Data Driven Improvement Model Emphasis on cultural diversity and cognitive processes in all child development courses. Create additional audio, video, and project- based assignments to accommodate all learners. Assess these two components in a variety of ways, at different points within each course, within the semester. 14

Career and Technical Educators Using a Data Driven Improvement Model Do not offer 8 week course time frame-too short, students don’t get started, get overwhelmed, withdraw, or fail. Schedule 15 week hybrid courses with on campus meeting once/week, remainder online. (done) 15