Online Instructors\u27 Use of the Cognitive Theory of Multimedia Learning Design Principles: A Mixed Methods Investigation

The use of digital video in online education is increasing alongside the growth of online learning in higher education in the United States driven in part by the COVID-19 pandemic (Bétrancourt & Benetos, 2018; McCormack, 2020; Seaman, et al, 2018). The study of digital instructional video is still at an early stage (Chorianopoulos, 2018) and current research has examined students and not instructors (Kay, 2012; Pan, et al, 2012). There are no studies solely focused on higher education instructors’ perspectives of digital video use for instruction (Kay, 2012). The purpose of this explanatory sequential mixed methods study (QUAN à qual) was to develop a case study describing instructor implementation of the 11 Cognitive Theory of Multimedia Learning (CTML) design principles in videos created for use in online courses (Mayer, 2019). The case study combined self-reported survey data from 55 online instructors, interview data from five instructors with the highest self-reported implementation of CTML design principle, and analysis data from five video artifacts. Results indicate that instructors are implementing the coherence, modality and voice principles with fidelity while the signaling, redundancy, segmenting, and embodiment principles are lagging. Themes from the interviewees suggest possible video creation techniques that can assist instructors in implementing all of the CTML design principles in future instructional videos

Online Instructors’ Use of the Cognitive Theory of Multimedia Learning Design Principles: A Mixed Methods Investigation

The growing use of digital video for online learning among US higher education instructors accelerated as a result of the COVID-19 pandemic raising questions about instructors’ knowledge of video creation principles (Bétrancourt & Benetos, 2018; Chorianopoulos, 2018; Kay, 2012; McCormack, 2020; Seaman, et al, 2018). This explanatory sequential mixed methods research describes the extent to which higher education instructors who create digital instructional video for online learning applied 11 multimedia design principles of the Cognitive Theory of Multimedia Learning (CTML). The case study triangulated self-reported survey data from 55 online instructors, interview data from five instructors with the highest implementation of CTML design principles as measured in the survey, and analysis of five video artifacts. Instructors implemented the CTML design principles more often than not, but applied certain principles like redundancy less frequently. Students and personal impacts are factors that informed instructor video design decisions and implementation of CTML design principles is driven more by instructors’ personal experiences and preferences rather than knowledge of the design principles. Given these findings, recommendations for instructors include continuing to be “video stars”, incorporating more signals into their videos, checking on-screen text to ensure it is used as little as possible, accounting for the time needed to create a video, and remembering that it is not the tool, but how they use it that matters

“It’s the story”: Online Animated Simulation of Cultural Competence of Poverty -- A Pilot Study

Purpose: In this pilot study, researchers explore an online animated simulation as an educational tool for emerging health professionals to promote cultural competence of poverty, food insecurity, and the Supplemental Nutrition Assistance Program.Methods: Researchers recruited participants in the allied health sciences for focus groups to explore the effectiveness of an online animation in promoting cultural competence of poverty, food insecurity, and public assistance programs. Participants were asked about their experience with the educational tool and changes in cultural competence regarding poverty, food insecurity, and the Supplemental Nutrition Assistance Program. Participants also responded to five survey questions about their experience of the educational tool and cultural competence of poverty. Transcripts from focus groups were coded according to the five constructs of the Campinha-Bacote model for cultural competence, and further coded for recurring themes within these constructs. Results: Eleven participants across four allied health professions including nutrition, occupational therapy, nursing and pre-physical therapy participated in two focus groups. Researchers found all five constructs of the Campinha-Bacote model in analysis of focus group transcripts, with awareness and desire expressed more frequently and intensely. Participants stated the animated simulation increased their empathy for people who experience poverty, food insecurity and who need public assistance programs. Conclusion: Researchers find that this online animated simulation was an effective tool to improve cultural competence of poverty for emerging healthcare professionals. Use of similar animations by educators of healthcare professionals may also change existing negative views towards those who rely on the Supplemental Nutrition Assistance Program benefits and reduce the barrier of stigma associated with the program

OptCDR: a general computational method for the design of antibody complementarity determining regions for targeted epitope binding

Antibodies are an important class of proteins with many biomedical and biotechnical applications. Although there are a plethora of experimental techniques geared toward their efficient production, there is a paucity of computational methods for their de novo design. OptCDR is a general computational method to design the binding portions of antibodies to have high specificity and affinity against any targeted epitope of an antigen. First, combinations of canonical structures for the antibody complementarity determining regions (CDRs) that are most likely to be able to favorably bind the antigen are selected. This is followed by the simultaneous refinement of the CDR structures' backbones and optimal amino acid selection for each position. OptCDR is applied to three computational test cases: a peptide from the capsid of hepatitis C, the hapten fluorescein and the protein vascular endothelial growth factor. The results demonstrate that OptCDR can efficiently generate diverse antibody libraries of a pre-specified size with promising antigen affinity potential as exemplified by computationally derived binding metrics. Keywords: antibody design/computational protein design/ fluorescein/hepatitis C/vascular endothelial growth facto

Combining different design strategies for rational affinity maturation of the MICA‐NKG2D interface

We redesigned residues on the surface of MICA, a protein that binds the homodimeric immunoreceptor NKG2D, to increase binding affinity with a series of rational, incremental changes. A fixed‐backbone RosettaDesign protocol scored a set of initial mutations, which we tested by surface plasmon resonance for thermodynamics and kinetics of NKG2D binding, both singly and in combination. We combined the best four mutations at the surface with three affinity‐enhancing mutations below the binding interface found with a previous design strategy. After curating design scores with three cross‐validated tests, we found a linear relationship between free energy of binding and design score, and to a lesser extent, enthalpy and design score. Multiple mutants bound with substantial subadditivity, but in at least one case full additivity was observed when combining distant mutations. Altogether, combining the best mutations from the two strategies into a septuple mutant enhanced affinity by 50‐fold, to 50 nM, demonstrating a simple, effective protocol for affinity enhancement.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93571/1/PRO_2115_sm_Suppinfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/93571/2/2115_ftp.pd

Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design

Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.National Institute of General Medical Sciences (U.S.) (Award R01 GM110048

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data

Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes

Alien Registration- Pantazes, John N. (Augusta, Kennebec County)

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