Reflection in alignment to professional standards: What did the student teachers highlight?

This study investigates the professional development of elementary student teachers in a teacher education program. Student teaching is a process for pre-service teachers to apply learning in an authentic school context, and one critical aspect of professional development is through reflection. The participants were primarily examined through their weekly reflections on teaching and learning experiences over an eight-week period. Using the state Standards for the Teaching Profession as a framework, the student teachers chose to reflect on topics they were most interested in exploring. Results indicated that the participants gave predominant attention to classroom management; the standards that received the least reflection were organizing curriculum and planning instruction. Analysis of the reflection journals also revealed how the student teachers grew as individuals and in interaction with others in a learning community. Based on the results, implications for teacher education are proposed. Limitations are also discussed.

A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective

Decision-making based on machine learning systems, especially when this decision-making can affect human lives, is a subject of maximum interest in the Machine Learning community. It is, therefore, necessary to equip these systems with a means of estimating uncertainty in the predictions they emit in order to help practitioners make more informed decisions. In the present work, we introduce the topic of uncertainty estimation, and we analyze the peculiarities of such estimation when applied to classification systems. We analyze different methods that have been designed to provide classification systems based on deep learning with mechanisms for measuring the uncertainty of their predictions. We will take a look at how this uncertainty can be modeled and measured using different approaches, as well as practical considerations of different applications of uncertainty. Moreover, we review some of the properties that should be borne in mind when developing such metrics. All in all, the present survey aims at providing a pragmatic overview of the estimation of uncertainty in classification systems that can be very useful for both academic research and deep learning practitioners.

Towards a Consistent Interpretation of AIOps Models

Artificial Intelligence for IT Operations (AIOps) has been adopted in organizations in various tasks, including interpreting models to identify indicators of service failures. To avoid misleading practitioners, AIOps model interpretations should be consistent (i.e., different AIOps models on the same task agree with one another on feature importance). However, many AIOps studies violate established practices in the machine learning community when deriving interpretations, such as interpreting models with suboptimal performance, though the impact of such violations on the interpretation consistency has not been studied. In this article, we investigate the consistency of AIOps model interpretation along three dimensions: internal consistency, external consistency, and time consistency. We conduct a case study on two AIOps tasks: predicting Google cluster job failures and Backblaze hard drive failures. We find that the randomness from learners, hyperparameter tuning, and data sampling should be controlled to generate consistent interpretations. AIOps models with AUCs greater than 0.75 yield more consistent interpretation compared to low-performing models. Finally, AIOps models that are constructed with the Sliding Window or Full History approaches have the most consistent interpretation with the trends presented in the entire datasets. Our study provides valuable guidelines for practitioners to derive consistent AIOps model interpretation.

Effect of Mobile Learning on the Optimization of Preschool Education Teaching Mode under the Epidemic

Mobile learning has become an efficient way to meet the needs of work learning in the epidemic situation because of its convenience, flexibility, and freedom. This paper studies and discusses the impact of mobile learning on learning education and preschool education in the epidemic. A mobile learning community resource sharing algorithm is used to explore the speed of the online learning community to obtain learning resources. The advantages of online learning are analyzed by comparing the speed of learning resources obtained in ordinary groups. In this research, the random offloading algorithm (ROA) is proposed to analyze the student response. The results revealed that majority of the students believed that mobile learning helps in learning subjects to a greater extent.

Influence of psychological safety and social presence on knowledge sharing behavior in higher education online learning environment

Purpose The absence of physical interactions in online learning environment brings psychological influences on learning participants in interacting and sharing knowledge with others, such as ignorance of other member’s presence and insecurity to share something in online environment. The purpose of this research was to examine the knowledge sharing behavior (KSB) by online learning community members in terms of their psychological safety (PS) and social presence (SP) perceptions. In addition, this research also identified the influence of PS to promote SP and the mediation impact of SP in the relationships between PS and KSB. Design/methodology/approach The data were gathered through self-administered questionnaire distributed to 133 online class members at a university in Indonesia where online learning has created a new learning experience. To represent key behavioral attributes, 12 items were used to represent PS, SP and KSB. The relationships among the variables were analyzed using the structural equation modelling method. Findings The result showed that PS positively influenced SP and KSB. SP also brought a positive impact on promoting KSB and fully mediated the relationship between PS and KSB. Research limitations/implications The result may not have fully captured the reflection of the influencing factors of KSB, as this research focused only on two psychological factors, namely, PS and SP. The research may be further enriched by including additional factors and expanding the data collection to include more online learning institutions. Practical implications The results implied the importance of PS and SP perception to promoting KSB in online learning environments. The results highlighted an important message to universities and schools to be more concerned on students’ feeling safe personally and students’ awareness of others’ presence to maximize knowledge sharing activities in online class environment. Originality/value This paper revealed the importance of PS and SP to promote KSB in the higher education online learning community. To the best of the researchers’ knowledge, this is the first study to link PS and SP to KSB and identify the importance of the mediation effect of SP on the relationship between PS and KSB specifically in higher education online learning environment.

Transnational Education and e-Learning during a Pandemic: Challenges, Opportunities, and Future

Higher education institutions are globally facing unprecedented disruptive trends, which have rapidly changed the landscape of global higher education due to the COVID-19 pandemic. While transnational education (TNE) is increasingly becoming popular as a provision for internationally recognised education at the doorstep of students, the temporary shift from traditional classroom teaching and learning (T&L) to remote online T&L caused by the COVID-19 pandemic has been challenging for all stakeholders to provide the similar student experience as previously. Regarding TNE programmes, the emergency replacement of traditional classrooms with virtual ones has also raised significant challenges of both equity and pedagogy. However, given the current crisis in higher education, TNE can be a cornerstone in rebuilding the post-COVID-19 international education system. This chapter explores the challenges faced by the TNE programmes based on a systematic literature review and information gathered informally from various stakeholders and discusses the opportunities and future impacts in teaching, learning, and student support as the post-COVID-19 educational landscape emerges. It also provides an insight into how a sustainable transnational learning community can be developed for the quality and sustainability of international higher education in this new decade.