Examining teachers’ technological pedagogical and content knowledge in the era of cloud pedagogy

With the ongoing innovation of instructional technologies there has been an emerging call to examine what types of knowledge teachers require to survive in the era of cloud pedagogy. In response to this call we proposed a research model – TLPACK – which is based on technological pedagogical content knowledge (TPACK), information communication technologies - technological pedagogical content knowledge (ICT-TPCK), and education technology, pedagogy and didactics, academic subject-matter discipline, educational psychology and educational sociology knowledge (TPACK-XL), to explore the types of knowledge that teachers at various levels – from kindergarten to post-secondary level – should equip themselves with in detail. TLPACK consists of five constructs (technology knowledge, learner knowledge, pedagogy knowledge, academic discipline, content knowledge, and context knowledge) but in total the TLPACK scale comprises 39 items. All items were converged based on the viewpoints of five experts from academia and practice following six rounds of the Delphi method, and the finalised version was prepared for reliability and validity examination. Proportional stratified sampling was adopted to conduct a questionnaire survey among teachers from kindergarten to post-secondary levels in Taiwan (n = 301). Rigorous statistical analyses were undertaken to examine the reliability and validity of this new model. Based on the results of statistical analyses, including item analysis, exploratory factor analysis, and confirmatory factor analysis, it is reasonable to state that the proposed TLPACK scale has good reliability and validity for practical use. The conclusion and limitations of this study were drawn based on the extracted results, and suggestions for future study are reported at the end of this report.Keywords: Delphi method; hospitality education; ICT-TPCK; TLPACK; TPACK; TPACK-X

Enhancing EFL Learners’ Self-Efficacy Beliefs of Learning English with Emoji Feedbacks in CALL: Why and How

Encouraging feedback positively affects learners’ self-efficacy; in language learning, self-efficacy predicts language learner performance and behavior. Our research involved three studies to expand knowledge about why and how we can enhance English as a Foreign Language (EFL) learners’ self-efficacy beliefs in online settings. In Study 1, based on an online survey with 310 participants, we ascertained the extent to which EFL learners with greater self-efficacy tend to challenge themselves by learning content that requires a proficiency level that is higher than their current proficiency. In Study 2, we recruited 120 EFL learners; the results indicate that positive feedback via emojis embedded in online courses could significantly boost EFL learners’ self-efficacy beliefs about learning English. Study 3 involved 35 participants and extended the understanding provided by the first two studies, showing that EFL learners not only like to use emojis for computer-mediated communication (CMC), but also prefer to receive them as feedback. This research adds to knowledge on “why” and “how” we can enhance EFL learners’ self-efficacy beliefs in online contexts. We systematically provide empirical evidence regarding the aforementioned issues and demonstrate that positive feedback through emojis has great potential to enhance EFL learners’ self-efficacy, even when such feedback is subliminal

Exploring The Molecular Design of Protein Interaction Sites with Molecular Dynamics Simulations and Free Energy Calculations

The significant work that has been invested toward understanding protein−protein interaction has not translated into significant advances in structure-based predictions. In particular redesigning protein surfaces to bind to unrelated receptors remains a challenge, partly due to receptor flexibility, which is often neglected in these efforts. In this work, we computationally graft the binding epitope of various small proteins obtained from the RCSB database to bind to barnase, lysozyme, and trypsin using a previously derived and validated algorithm. In an effort to probe the protein complexes in a realistic environment, all native and designer complexes were subjected to a total of nearly 400 ns of explicit-solvent molecular dynamics (MD) simulation. The MD data led to an unexpected observation: some of the designer complexes were highly unstable and decomposed during the trajectories. In contrast, the native and a number of designer complexes remained consistently stable. The unstable conformers provided us with a unique opportunity to define the structural and energetic factors that lead to unproductive protein−protein complexes. To that end we used free energy calculations following the MM-PBSA approach to determine the role of nonpolar effects, electrostatics and entropy in binding. Remarkably, we found that a majority of unstable complexes exhibited more favorable electrostatics than native or stable designer complexes, suggesting that favorable electrostatic interactions are not prerequisite for complex formation between proteins. However, nonpolar effects remained consistently more favorable in native and stable designer complexes reinforcing the importance of hydrophobic effects in protein−protein binding. While entropy systematically opposed binding in all cases, there was no observed trend in the entropy difference between native and designer complexes. A series of alanine scanning mutations of hot-spot residues at the interface of native and designer complexes showed less than optimal contacts of hot-spot residues with their surroundings in the unstable conformers, resulting in more favorable entropy for these complexes. Finally, disorder predictions revealed that secondary structures at the interface of unstable complexes exhibited greater disorder than the stable complexes