<p> Recent advances in technology and ideology have unlocked entirely new directions for education research. Mounting pressure from increasing tuition costs and free, online course offerings are opening discussion and catalyzing change in the physical classroom. The flipped classroom is at the center of this discussion. The flipped classroom is a new pedagogical method, which employs asynchronous video lectures, practice problems as homework, and active, group-based problem-solving activities in the classroom. It represents a unique combination of learning theories once thought to be incompatible--active, problem-based learning activities founded upon constructivist schema and instructional lectures derived from direct instruction methods founded upon behaviorist principles. The primary reason for examining this teaching method is that it holds the promise of delivering the best from both worlds. A controlled study of a sophomore-level numerical methods course was conducted using video lectures and model-eliciting activities (MEAs) in one section (treatment) and traditional group lecture-based teaching in the other (comparison). This study compared knowledge-based outcomes on two dimensions: conceptual understanding and conventional problem-solving ability. Homework and unit exams were used to assess conventional problem-solving ability, while quizzes and a conceptual test were used to measure conceptual understanding. There was no difference between sections on conceptual understanding as measured by quizzes and concept test scores. The difference between average exam scores was also not significant. However, homework scores were significantly lower by 15.5 percentage points (out of 100), which was equivalent to an effect size of 0.70. This difference appears to be due to the fact that students in the MEA/video lecture section had a higher workload than students in the comparison section and consequently neglected to do some of the homework because it was not heavily weighted in the final course grade. A comparison of student evaluations across the sections of this course revealed that perceptions were significantly lower for the MEA/video lecture section on 3 items (out of 18). Based on student feedback, it is recommended that future implementations ensure tighter integration between MEAs and other required course assignments. This could involve using a higher number of shorter MEAs and more focus on the early introduction of MEAs to students.</p>

<p> The purpose of this study was to determine if significant relationships exist for any of the variables, age, gender, previous GPA, test scores (ACT, Compass), number of accumulated credits, and student success in Biology. This study strived to determine what academic/admissions data can be used to determine the likelihood of student success in Biology. A quantitative correlational study using stepwise multiple regression analysis was used for this study. The study was a retrospective study. Data was composed of a convenience archival sample from the institutional database. Multiple regression analysis was conducted to determine the effect each independent variable has on the dependent variable of student success. For the data set ACT, the variables math score, prealg score, writing score, reading score, and previous GPA were all significant. For data set CMP the variable of student's age was not significant, but the other variables were significant. For the Blanks data set, the only variable of significance was gender. Using stepwise multiple regression analysis the data sets produced regression models showing predictability based on stepwise significance. For Blanks data set, the variables previous hours earned, gender, age, and previous GPA were used. For the ACT data set, math score and reading score were used. For the CMP data set the variables included math score, writing score, previous GPA, gender, reading score, and previous hours earned. The level of predictability of the regression equation for the ACT data set and Blank data set was low. However, the predictability for the CMP data set was moderate. The highest percent of variance explained by the regression models was 11.6% of the CMP data set.</p>

Thesis (Ph.D.)--Indiana University, Dept. of Curriculum and Instruction in the School of Education, 2007. / Source: Dissertation Abstracts International, Volume: 68-07, Section: A, page: 2882. Adviser: Valarie L. Akerson. Title from dissertation home page (viewed Apr. 14, 2008).

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 3041. Adviser: Douglas H. Beck. Includes bibliographical references (leaves 182-185) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ed.D.)--Lehigh University, 2007. / Adviser: H. Lynn Columba-Piervallo.

Thesis (Ph.D.)--Indiana University, Dept. of Curriculum and Instruction, 2009. / Title from PDF t.p. (viewed on Oct. 6, 2009). Source: Dissertation Abstracts International, Volume: 70-02, Section: A, page: 0518. Adviser: Valarie Akerson.

Thesis (Ed.D.)--Indiana University, School of Education, 2005. / Title from PDF t.p. (viewed Dec. 8, 2008). Source: Dissertation Abstracts International, Volume: 66-04, Section: A, page: 1313. Adviser: Charles Barman.

Thesis (Ph.D.)--Indiana University, Dept. of Currulum and Instruction, 2005. / Source: Dissertation Abstracts International, Volume: 66-06, Section: A, page: 2159. Adviser: William Harwood. "Title from dissertation home page (viewed Nov. 27, 2006)."

Thesis (Ph.D.)--Indiana University, Dept. of Educational Leadership and Policy Studies, 2005. / Source: Dissertation Abstracts International, Volume: 66-04, Section: A, page: 1289. Adviser: Thomas A. Schwandt. "Title from dissertation home page (viewed Nov. 9, 2006)."

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-11, Section: A, page: 4077. Adviser: Walter Feinberg. Includes bibliographical references (leaves 184-194) Available on microfilm from Pro Quest Information and Learning.