Mixing in-class and online learning: Content meta-analysis of outcomes for hybrid, blended, and flipped courses

Over the past 15 years, courses that mix face-to-face and online instructional methods, such as blended, hybrid, and flipped courses, have gained both supporters and skeptics in higher education. Studies that compare mixed courses to face-to-face or online courses have conflicting results: some find improved learning outcomes and some find no significant differences. We contend that these conflicting results are due to inconsistent or vague definitions of hybrid, blended, and flipped. To address this problem, we use the definitions from a recently proposed taxonomy to reclassify studies in the literature. After reclassification, analysis of this literature reveals two main themes that illuminate how mixed instructional methods affect learning outcomes. Courses that use mixed methods can either reduce time in class and maintain learning outcomes or maintain time in class and improve learning outcomes

A Taxonomy to Define Courses that Mix Face-to-Face and Online Learning

The efficacy of courses that mix face-to-face and online instruction, such as blended, hybrid, flipped, and inverted courses, is contested in the literature. Some studies find that they improved learning outcomes and some do not. We argue that these unreliable results are due to inconsistent definitions of these courses. To address this problem, we propose the Mixed Instructional eXperience (MIX) taxonomy to define hybrid, blended, flipped, and inverted based on two dimensions. To test the usefulness of the taxonomy to organize the literature, we reclassified research using the taxonomy. The analysis of the literature after reclassification revealed themes that illuminate how mixing face-to-face and online instruction affects learning. These findings validate the taxonomy as a useful tool for classifying literature and further knowledge in this field

Improving student learning in an introductory programming course using flipped classroom and competency framework

The author Joelle Elmaleh was publishing under the name Joelle Ducrot.</p

Heterogeneity-Aware Digital Design of an Introductory Module in Computer Science

In Einführungsmodulen, die sowohl als Fachmodul als auch als Importmodul für verschiedene Studiengänge angeboten werden, unterscheiden sich die Vorkenntnisse, Ziele und Motivationen der Teilnehmenden erheblich. Im Zuge eines Projekts zur Digitalisierung der Lehre in der Informatik wurde daher über zwei Jahre ein Konzept für ein solches Einführungsmodul der Informatik entwickelt, das individuellere Lernangebote bereithält und synchrone mit asynchronen Lernphasen verbindet. Dabei wurden die Konzepte des Flipped Classroom und der Projektarbeit als didaktische Basis verwendet. Als Programmierumgebung und zur Bereitstellung der Lernmaterialien wurden Jupyter Notebooks genutzt. Durch verschiedene Evaluationsmethoden sowohl mit den Studierenden als auch mit den studentischen Tutor:innen, die diese betreuen, konnten nach dem ersten Jahr Aspekte identifiziert werden, die im zweiten Jahr weiter verbessert wurden. Trotz der Belastungen durch das digitale Coronasemester ist ergänzt um einen Gamification-Ansatz ein Konzept entstanden, das Studierenden individuelle Lerngelegenheiten bietet und zu einer hohen Zufriedenheit führt. Insbesondere scheint es durch asynchrone Lernmaterialien gelungen zu sein, die bei klassischen Vorlesungen häufig fehlende Vor- und Nachbereitung der Studierenden zu fördern.In introductory modules offered for different degree programmes with different impact on the study plan, the previous knowledge, goals and motivations of the participants differ considerably. As a part of a project on the digitalisation of teaching in computer science, a concept for such an introductory computer science module was therefore developed over two years. It provides individualised learning opportunities and combines synchronous with asynchronous learning phases. The concepts of flipped classroom and project work were used as a didactical basis, while Jupyter Notebook was used as a programming environment and for providing learning material. Through various evaluation methods involving both the students and the student tutors, who supervise them, it was possible to identify aspects that were improved further in the second year after the first year. Despite the burdens of the digital semester caused by the COVID-pandemic, a concept has been developed that offers more individual learning opportunities to the students and leads to a high level of satisfaction. A gamification concept was also included. In particular, asynchronous learning materials seem to have succeeded in encouraging students to prepare and follow up on their learning, which is often lacking in traditional lectures

Flipped Classrooms versus Traditional Classrooms: A systematic review and meta-analysis of student achievement in higher education

In an attempt to understand what makes blended learning (BL) more effective than Classroom Instruction (CI), this research looked more closely at the Flipped Classroom (FC) model of BL. The FC takes a relatively consistent approach to course design by flipping what is traditionally done in the classroom (i.e., lecture) with what is traditionally done as homework (i.e., application). Numerous studies have been conducted comparing FC with the CI on student achievement in higher education without conclusive results. To synthesize the literature, this dissertation implemented a systematic review and meta-analysis to measure the average effect size and the direction of the impact and to determine the conditions under which students learn more effectively. To ensure a transparent process the potential for bias in each step of a meta-analysis was acknowledged and addressed. Through a systematic review of the literature from 2000 to 2017, 114 studies were included and 125 effect sizes were calculated. Using meta-analysis these effect sizes created a weighted mean effect-size of +0.30, which was statistically significant at p < 0.05 and educationally significant. Study features were analyzed to determine if there were any attributes that made a difference but none were found to be significant. The use of quizzes, however, showed an interesting pattern and near significant difference (p = .058) when the effect sizes were grouped by STEM, non-STEM and Health-related disciplines. No publication bias was found, no outliers were found from the sensitivity analysis, and there was no significant difference between the effects from quasi-experimental and experimental designs. While the FC significantly outperformed CI it was not to a greater extent than general BL outperformed CI. Future research is encouraged between levels of treatments, instead of between FC and CI, in order to provide more nuanced results about how to improve instructional design in future courses

Defining the Competencies, Programming Languages, and Assessments for an Introductory Computer Science Course

The purpose of this study was to define the competencies, programming languages, and assessments for an introductory computer science course at a small private liberal arts university. Three research questions were addressed that involved identifying the competencies, programming languages, and assessments that academic and industry experts in California’s Central Valley felt most important and appropriate for an introduction to computer science course. The Delphi methodology was used to collect data from the two groups of experts with various backgrounds related to computing. The goal was to find consensus among the individual groups to best define aspects that would best comprise an introductory CS0 course for majors and non-majors. The output would be valuable information to be considered by curriculum designers who are developing a new program in software engineering at the institution. The process outlined would also be useful to curriculum designers in other fields and geographic regions who attempt to address their local education needs. Four rounds of surveys were conducted. The groups of experts were combined in the first round to rate the items in the straw models determined from the literature and add additional components when necessary. The academic and industry groupings were separated for the remainder of the study so that a curriculum designer could determine not only the items deemed most important, but also their relative importance among the two distinct groups. The experts selected items in each of the three categories in the second round to reduce the possibilities for subsequent rounds. The groups were then asked to rank the items in each of the three categories for the third round. A fourth round was held as consensus was not reached by either of the groups for any of the categories as determined by Kendall’s W. The academic experts reached consensus on a list of ranked competencies in the final round and showed a high degree of agreement on lists of ranked programming languages and assessments. Kendall’s W, values, however, were just short of the required 0.7 threshold for consensus on these final two items. The industry experts did not reach consensus and showed low agreement on their recommendations for competencies, programming languages, and assessments