Secondary implementation of interactive engagement teaching techniques: Choices and challenges in a Gulf Arab context

We report on efforts to design the “Collaborative Workshop Physics” (CWP) instructional strategy to deliver the first interactive engagement (IE) physics course at Khalifa University of Science, Technology and Research (KU), United Arab Emirates (UAE). To our knowledge, this work reports the first calculus-based, introductory mechanics course on the Arabian Peninsula using physics education research (PER)-based instruction. A brief history and present context of general university and STEM teaching in the UAE is given. We present this secondary implementation (SI) as a case study of a novel context and use it to determine if PER-based instruction can be successfully implemented far from the cultural context of the primary developer and, if so, how might such SIs differ from SIs within the United States (U.S.) in terms of criteria for and risks to their success. With these questions in view, a prereform baseline comprised of Maryland Physics Expectations in Physics survey, Force Concept Inventory (FCI), course exam grades, and English language proficiency data are used to design a hybrid implementation of Cooperative Group Problem Solving. We find that for students with high English proficiency, normalized gain on FCI improves substantially, from $⟨g⟩=0.16\pm 0.10$ prereform to $⟨g⟩=0.47\pm 0.08$ in the CWP pilot (standard errors), indicating a successful SI. However, we also find evidence that normalized gains on FCI are strongly modulated by language proficiency and discuss likely causes. Regardless of language ability, problem-solving skill is also substantially improved and course drop-fail-withdrawal rates are cut from 50% to 24%. In particular, we find evidence in postreform student interviews that prior classroom experiences, and not broader cultural expectations about education, are the more significant cause of expectations that are at odds with the classroom norms of well-functioning PER-based instruction. We present this result as evidence that PER-based innovations can be implemented across great changes in cultural context, provided that the method is thoughtfully adapted in anticipation of context and culture-specific student expectations. This case study should be valuable for future reforms at KU, the broader Gulf region, and other institutions facing similar challenges involving SI of PER-based instruction outside the U.S.

Figure 1

Improvements to FCI (or FMCE) normalized gain, as a result of a PER-based course reform, for the national cultural contexts of the reforms. Error bars for Primary U.S. and Secondary U.S. cases are standard errors in the average, taken over the available cases in the literature. AUS, PUE, and UAE represent single SIs conducted for predominantly Australian, Puerto Rican, and Emirati student populations, respectively. Above each entry, the rank out of 79 is given for “cultural distance” relative to U.S. national culture, computed by calculating the geometric distance from the U.S. score on Hostede’s dimensions and ranking the results in ascending order. A low rank indicates relative cultural similarity to U.S. national culture, in Hofstede’s framework. A high rank indicates relative dissimilarity. Improvement to FCI normalized gain is replotted (purple, cross-hatched) for UAE, for only those students in the CWP reform with high English language proficiency (i.e., IELTS score).

Figure 2

The favorable-unfavorable Dalitz-style plot for preinstruction MPEX survey data comparing the Calibration Group (blue squares) and U.S. Tertiary student groups (red circles) from the original MPEX study [44] to KU students (green triangle), and students from data sets in other high-PDI nations (India, Turkey, Thailand, and Philippines)[45]. KU student data include that for the year of (2011) and the year prior to (2010) the CWP reform. The MPEX overall score (top) is compared to the question subsets for the independence (middle) and effort (bottom) clusters. The average for random responses (black diamond) is shown for benchmarking purposes.

Figure 3

Histogram of pretest (black) and post-test (white) Force Concept Inventory data. (a) FCI data from all traditionally taught students. (b) Data from all traditionally taught, directly admitted students. (c) Data from all traditionally taught, conditionally admitted students. (d) Data from all conditionally admitted students taught in the CWP pilot.

Figure 4

Course exam averages (top) and FCI normalized gains (bottom) plotted versus language proficiency, as measured by IELTS overall score (bottom horizontal axis). Vertical error bars are standard errors in the mean for these data. An approximate TOEFL iBT score adapted from Table 7 of Ref. [14] and displayed (top horizontal axis). Horizontal error bars are approximated by a linear fit ($R=0.978$) to this table [14] and do not necessarily represent the uncertainty in student language proficiency, but rather the relative uncertainty between IELTS and TOEFL iBT scales. For FCI normalized gains (bottom), Hake’s result [16] for the average and standard error interval for traditional (blue, left-hatched) and IE (red, right-hatched) pedagogy are highlighted. The average and standard error interval for random responses (purple, cross-hatched) is highlighted for benchmarking purposes.

Figure 5

A flowchart representation, timeline, and description of tasks in the three-hour Collaboration Workshop.

Figure 6

An example sequence of activities in a CWP session, in this case for instruction on linear momentum.