Purpose The purpose of this study is to examine student sentiments regarding high-quality vs low-quality teaching. Design/methodology/approach This study uses a text mining technique to identify the positive and negative patterns of student sentiments from student evaluations of teaching (SET) provided on Ratemyprofessors.com. After identifying the key positive and negative sentiments, this study performs generalized linear regressions and calculates cumulative logits to analyze the impact of key sentiments on high- and low-quality teaching. Findings Results from 6,705 SET provided on Ratemyprofessors.com indicated that students express different sets of sentiments regarding high- vs low-quality teaching. In particular, the authors found positive sentiments such as passionate, straightforward, accessible, hilarious, sweet, inspiring and clear to be predictive of high-quality teaching. Additionally, negative sentiments such as disorganized, rude, difficult, confusing and boring were significantly related to low-quality teaching. Originality/value This study is one of the first few studies confirming that high- and low-quality teaching are not completely opposite to each other from the student’s perspective. That is, the presence of high-quality teaching does not necessarily mean the absence of low-quality teaching. As such, this study provides an important theoretical base for future researchers who wish to explore approaches for improving faculty teaching in the higher education setting. Additionally, this study offers educators some recommendations that may help students experience positive sentiments while minimizing negative sentiments.

This paper provides a critical review on the advancements of artificial intelligence in recent applications in building environments from the perspectives of key research hotpots, important research institutes, researchers, and their contributions. Associated technologies, such as Internet of things (IOT) technologies, and advanced operational strategies for promoting building performance are also discussed in the paper. Bibliometric analysis on the platform CiteSpace quantitatively summarizes the key characteristics of works in the literature and their applications. IOT based sensing networks are analyzed, discussed, and summarized since they play a pivotal role in securing the accuracy and efficiencies in data acquisition so as to facilitate building energy management systems. Additionally, the algorithms associated with machine learning and data-driven technologies are reviewed in the applications such as building energy prediction, building management optimization, and their maintenance. This paper explores the emerging technologies and developing trends in the field so as to find potential routes for future studies (which will encourage the uptake of AI technologies in buildings).