Evaluation of the mixed-mode teaching design upon students' learning satisfaction and academic performance in an introductory economics course

The Covid-19 pandemic has had a major impact on the various facets of higher education globally. With the migration to online teaching happening at an unprecedented rate, educators are challenged in transforming the way they create opportunities for students' learning. Specifically, in Australia, education providers have increasingly offered their courses in a dual-mode setting, making them available for both online and face-to-face students. This paper presents the design of a specific type of dual-mode teaching, referred to as mixed-mode teaching used in an introductory economics course at X University. The aim of this study is to evaluate the impact of the mixed-mode teaching in creating an equitable learning experience for the online and face-to-face groups of students enroled in the course. Such an approach should then translate into there being no significant difference found in the academic performance of the two cohorts. In this study, we used the non-parametric Wilcoxon test and Kruskal-Wallis test to verify if a significant difference exists in learning satisfaction. Further, we utilised regression with dummies, and four different approaches of propensity score matching estimation in excluding self-selection bias, to evaluate differences in academic performance. Our results suggest no statistically significant differences in both the learning experiences and academic performances of our two groups of students. At a time when higher education is facing ongoing challenges presented by the pandemic, these findings offer useful insights for economics educators as well as those in higher education about how to enhance students' academic performance and learning experience through more equitable, consistent course design. © 2023 Elsevier Ltd

A scalable and high yielding SARS-CoV-2 spike protein receptor binding domain production process.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike protein is of interest for the development of vaccines and therapeutics against COVID-19. Vaccines are designed to raise an immune response against the spike protein. Other therapies attempt to block the interaction of the spike protein and mammalian cells. Therefore, the spike protein itself and specific interacting regions of the spike protein are reagents required by industry to enable the advancement of medicines to combat SARS-CoV-2. Early production methods of the SARS-CoV-2 spike protein receptor binding domain (RBD) were labor intensive with scalability challenges. In this work, we describe a high yielding and scalable production process for the SARS-CoV-2 RBD. Expression was performed in human embryonic kidney (HEK) 293 cells followed by a two-column purification process including immobilized metal affinity chromatography (IMAC) followed by Ceramic Hydroxyapatite (CHT). The improved process showed good scalability, enabling efficient purification of 2.5 g of product from a 200 L scale bioreactor.

The Impact of Survey Mode Design and Questionnaire Length on Measurement Quality

Mixed-mode surveys are popular as they can save costs and maintain (or improve) response rates relative to single-mode surveys. Nevertheless, it is not yet clear how design decisions like survey mode or questionnaire length impact measurement quality. In this study, we compare measurement quality in an experiment of three distinct survey designs implemented in the German sample of the European Values Study: a single-mode face-to-face design, a mixed-mode mail/web design, and a shorter (matrix) questionnaire in the mixed-mode design. We compare measurement quality in different ways, including differences in distributions across several data quality indicators as well as equivalence testing over 140 items in 25 attitudinal scales. We find similar data quality across the survey designs, although the mixed-mode survey shows more item nonresponse compared to the single-mode survey. Using equivalence testing we find that most scales achieve metric equivalence and, to a lesser extent, scalar equivalence across the designs.

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN.

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

Research and Practice of Curriculum Teaching Reform in Colleges and Universities in Post-epidemic Era

Affected by the epidemic situation of COVID-19, colleges and universities have adopted online teaching methods one after another, so teachers and students have the opportunity to experience the whole online teaching process. After the COVID-19 come to an end, online teaching will continue to have a corresponding impact on teachers' teaching and students' learning, and will also promote the deepening reform of online teaching. The combination of online and offline modes of instruction is bound to become the normalization mode of college curriculum teaching in the post-epidemic era. Taking the course 'Fundamentals of Programming' as an example, this paper makes an attempt to implement 'online and offline mixed' teaching, so as to study and practice the curriculum reform in the post-epidemic era. © 2022 IEEE.

Omicron BA.2 Prediction Research Based on SEIR-ARIMA Mixed Model

Omicron BA.2, a new variant of severe acute respiratory syndrome coronavirus (SARS-CoV-2), has attracted worldwide attention due to its high infectivity and vaccine escape mutation. Based on the SEIR model being susceptible to changes in external factors and having specific errors, the ARIMA model is data-dependent and can only capture linear relationships. In this paper, based on the traditional infectious disease dynamic model SEIR and the differential integrated mean autoregressive model ARIMA, an SEIR-ARIMA mixed model is proposed to predict and evaluate the virus outbreak in March in Jilin Province, China. The data from SEIR and ARIMA models were processed using SPSS to obtain the predicted values f and e, respectively. Linear regression modeling was performed on the predicted values f and e to establish the SEIR-ARIMA model. MATLAB is used to complete the best linear fitting line. Furthermore, The results show that the model's predicted value is in good agreement with the actual value. It shows that the SEIR-ARIMA mixed model based on the SEIR-ARIMA model has a good prediction effect, which is beneficial for the country to make the right decision when facing the epidemic. It is of great value for preventing other types of infectious diseases in China in the future. © 2022 IEEE.

Faculty Members' Evaluations Regarding the Faculty Development Program Conducted with the Hybrid Learning Method

Objective: This study aims to determine faculty members' feedback and the level of satisfaction about the faculty development program carried out with the hybrid learning method at Selçuk University Faculty of Medicine (SUFoM) and compare faculty members' satisfaction levels regarding face-to-face and online sessions. Material and Methods: The Faculty Development Program has been carried out since 2010 and updated with program evaluations as well as faculty members' needs and expectations. In the COVID-19 pandemic era, distance education and hybrid education sessions were added to the program. The renewed faculty development program was conducted in June 2021 with a hybrid learning method, eight sessions both face-to-face and online and six sessions online only. The feedback of the participants was obtained through online questionnaires consisting of structured items rated with Likert-type scales (1: Strongly disagree -- 5: Strongly agree and 0: Very poor -- 10: Very good) and semi-structured items. Results: Fifty faculty members participated in the program and 30 (60.0%) answered the research questionnaires. Faculty members reported a total of 170 session participation, 140 (82.4%) face-toface and 30 (17.6%) online. It was determined that the participants' satisfaction levels about the faculty development program sessions was high and very high (min=4.05±0.99 ;max=5.00±0.00). It was also found that there is no statistically significant difference between faculty members evaluations for online or face-to-face participation in the training sessions. Participants reported that their overall evaluation of the program was very good (9.33±0.65). Conclusion: The SUFoM Faculty Development Program, designed in accordance with the COVID-19 conditions, has been successfully implemented using the hybrid method. The positive feedback of faculty members and the fact that online or face-to-face participation in the sessions does not make a difference in their satisfaction levels is considered as an important development for the implementation of the forthcoming faculty development program using the hybrid method. (English) [ FROM AUTHOR] Amaç: Bu araştırmada, Selçuk Üniversitesi Tıp Fakültesinde (SÜTF) hibrit öğrenme yöntemi ile gerçekleştirilen eğitici gelişimi programına ilişkin katılımcı öğretim üyelerinin geri bildirimlerinin alınması ve yüz yüze ve çevrim içi (online) gerçekleştirilen eğitim etkinliklerine ilişkin beğeni düzeylerinin belirlenmesi ve karşılaştırılması amaçlanmıştır. Gereç ve yöntem: Eğitici Gelişimi Programı, SÜTF'de 2010 yılından bu yana öğretim üyelerinin beklenti ve gereksinimleri ile programın değerlendirilme verileri ışığında güncellenerek uygulanmıştır. Programa COVID-19 pandemisi döneminde uzaktan eğitim ve hibrit eğitimin SÜTF'de uygulanmasıyla ilgili oturumlar eklenmiştir. Yenilenen eğitici gelişimi programı Haziran 2021'de, hibrit öğrenme yöntemi ile sekiz oturum hem yüz yüze hem de çevrim içi, altı oturum ise yalnızca çevrim içi olacak biçimde sunulmuştur. Katılımcıların geri bildirimleri Likert tipi ölçekler (1: Kesinlikle katılmıyorum -- 5: Kesinlikle katılıyorum ve 0: Çok kötü -- 10:Çok iyi) ile değerlendirilen yapılandırılmış maddeler ve yarı yapılandırılmış maddelerden oluşan çevrim içi anket formlarıyla alınmıştır. Bulgular: Programa 50 öğretim üyesi katılmış ve 30'u (%60,0) araştırma anket formlarını yanıtlamıştır. Öğretim üyeleri, 140 (%82,4) yüz yüze, 30 (%17,6) çevrim içi olmak üzere toplam 170 oturum katılımı bildirmişlerdir. Katılımcıların eğitici gelişimi programı oturumlarına ilişkin beğenilerinin yüksek ve çok yüksek düzeyde gerçekleştiği saptanmıştır (min=4,05±0,99 ;maks=5,00±0,00). Öğretim üyelerinin bu değerlendirmelerde verdiği puanların, eğitim oturumlarına çevrim içi veya yüz yüze katılma durumuna göre istatistiksel olarak anlamlı farklılık göstermediği saptanmıştır. Katılımcılar programına ilişkin genel değerlendirmelerinin çok iyi olduğunu (9,33±0,65) bildirmiştir. Sonuç: COVID-19 koşullarına uygun olarak tasarlanan SÜTF Eğitici Gelişimi Programı hibrit yöntem kullanılarak başarı ile uygulanmıştır. Öğretim üyelerinin olumlu geri bildirimleri ve oturumlara çevrim içi veya yüz yüze katılmanın beğeni düzeylerinde fark oluşturmaması önümüzdeki dönemde de eğitici gelişimi programının hibrit yöntem kullanılarak uygulanması için yönünde önemli bir gelişme olarak değerlendirilmektedir. (Turkish) [ FROM AUTHOR] Copyright of Genel Tip Dergisi is the property of Genel Tip Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Prediction of Students’ Performance Based on Their Learning Experiences and Assessments: Statistical and Neural Network Approaches

The education approaches in the higher education have been evolved due to the impact of covid-19 pandemic. The predicting of students’ final performance has become more crucial as various new learning approaches have been adopted in the teaching. This paper proposes a statistical and neural network model to predict students’ final performance based on their learning experiences and assessments as the predictor variables. Students’ learning experiences were obtained through educational data analytic platform on a module that delivered the mixed-mode education strategy using Flipped classroom, asynchronous and cognitive learning in combination with the revised Bloom’s taxonomy. Statistical evaluations including multiple regressions, ANOVA correlations are performed to evaluate the appropriateness of the input variables used for the later Neural Network output prediction. The Levenberg-Marquardt algorithm is employed as the training rule for the Neural Network model. The performance of neural network model is further verified to prevent the overfitting issue. The Neural Network model has achieved a high prediction accuracy justifying the students’ final performance through utilising the aforementioned pedagogical practises along with limitations. © 2022, Springer Nature Switzerland AG.

Work-in-Progress - Post-COVID: Adapting Education to a Changing Educational Environment through Immersive Technology

In a Post-COVID world, Hybrid classes remain essential as students face challenges in attending them in person. This creates additional engagement issues. The creation of an easy-to-use platform to enable this mixed-mode classroom is critical for teachers that are frustrated by the difficult management of hybrid classrooms. An aspect is student engagement, which today has no established analytical mechanism to evaluate during classroom sessions. This work-in-progress paper describes a technology that has the ability to obtain real-time analytic data to determine the engagement of all students at all times and helps teachers to know which students are lagging early on. © 2022 Immersive Learning Research Network.

Lessons Learned in a Mixed-mode Teaching Experience

The coronavirus pandemic altered the teaching delivery modes for universities nationwide and in doing so, allowed for positive adaptation of the classroom experience. At the University of Tennessee at Knoxville (UTK), five different teaching modalities were offered to the student population for both the Fall 2020 and the Spring 2021 terms. Courses offered in new modalities were improved through implementation of new techniques in engagement, lesson delivery, and assessment. Specifically, enhancements were developed in three different types of courses: the technical communications course, laboratory courses, and a series of project-based courses. The technical communications course was changed to a rotating face-to-face model, for which lecture videos and assigned activities were performed on out-of-class days and in-class days were reserved for workshops. Workshops replaced the traditional guided learning activity approach with active learning in a think-pair-share format. Students were given strong and weak examples of writing to be able to give feedback to their peers and improve their own writing prior to submission. Students worked on improving their formal written assignments, and therefore improved their capacity for technical writing, during class rather than submitting their first writing assignment without any peer feedback or review. The workshop format also prevented students from attempting to write the entire paper the night before it was due, as they were required to submit regular progress check-ins in the weeks leading up to the due date. One project-based course was modified to incorporate an ePortfolio to improve records-keeping by the students in the mixed-mode learning experience and project experiences in the senior design project courses were enhanced through online modules supporting lesson content paired with workshops generating discussions-based learning. Assessment of learning in the project-based courses included a variety of new techniques, including professor-student interviews, guided discussion board engagement, and prompted video narratives. Lastly, laboratory courses were moved to a rotating hybrid system by splitting larger exercises into online and in-person components. This allowed for additional reinforcement of theoretical understanding and smaller in-person sessions promoting more one-on-one student contact. A peer review component was added to the course rubric to facilitate additional online student-lead learning opportunities. One upper division geotechnical laboratory course was converted into a semester-long project with group reporting and bi-weekly individual oral examinations. In this model all students were responsible for all course content, but teamwork and collaboration were encouraged and monitored through a mandatory online file sharing platform created for each project team. The teaching modality change for these courses presented an opportunity to improve the learning experience and the impact in these specific courses is particularly relevant as these present many fundamental skills necessary to be transferred to new learning experiences in later coursework. A summary of the teaching modifications for these three families of courses is presented herein;motivation for changes, implementation of the changes, and some reflective observations made by the faculty are shared. © American Society for Engineering Education, 2021

Teaching Structures in an (Almost) Empty Room: An Assessment of Strategies for Student Engagement in Mixed-mode and Remote Classes

Newly imposed educational delivery modalities such as mixed-mode or fully remote instruction due to the Covid-19 pandemic have resulted in creative, innovative instructional approaches to undergraduate engineering education. However, given the unique circumstances caused by the pandemic and the constraints it placed on students, some instructional techniques have been more successful than others. It is crucial for future efforts in remote and hybrid teaching environments to use this opportunity to document the realized benefits, unforeseen negative consequences, and student perceptions of various teaching strategies. This paper traces lessons learned through mixed-mode and remote instruction of structural engineering courses for three different courses and student populations: (1) introduction to steel design and (2) indeterminate analysis for structural engineering concentrators, and (3) steel and concrete design for engineering concentrators in other sub-disciplines. Across these courses, initial teaching strategies included a mixture of flipped classroom, traditional lectures, and interactive group problem solving. Collectively, the instructors determined through ongoing formal and informal student surveys, as well as additional unstructured feedback, that proposed teaching strategies required adjustments as the semester progressed. Some technological limitations were discovered after rigorous testing with live students, while successful technological strategies included digital problem sessions with document cameras, and chat-based questions with discussions. Furthermore, depending on course size and student population, students tended to engage more readily compared to verbal questions directed to the instructor during remote live classes. This engagement varied among written e-mails and chats, discussion boards, and Teaching Assistant (TA) office hours. To build on initial findings from individual course feedback, all three classes were evaluated using a common mid-term and end-of-term survey soliciting student reactions to content delivery, technology aides, and interactions with instructors/TAs. Overall, lessons learned through mixed-mode and remote instruction in structural engineering can inform future educators in this field, reducing time spent surveying available technologies and pointing towards strategies shown to be effective in this context. © American Society for Engineering Education, 2021