Maintaining Effort and Interest despite Challenges during the COVID-19 Pandemic: A Process Tracing Approach to a Teacher’s Grit during an Online L2 Course

Grit—the ability to maintain effort and interest for long-term goals—is argued to be an important individual factor for achievement, especially in the face of obstacles. However, little research has examined the possible fluctuations of effort and interest and how challenges may trigger the changes of effort and interest. In this study, we measured a teacher’s grit at the beginning of an online course during the COVID-19 pandemic, and we focused on the changes in a teacher’s effort and interest throughout the course. In this case study we unpacked the explanations of possible changes in grit via process tracing. Despite the fact that the teacher scored high on the grit scale, we found that the sudden shift from in-person to online teaching had put much pressure and demand on the teacher. The new teaching challenge influenced the teacher’s self-evaluation of their teaching performance and students’ engagement, which led to changes in effort and interest. Therefore, we argue that one’s average grit (e.g., measured by grit scale) cannot be the representation of their ability to maintain interest and effort on different occasions due to the influence of different situational causes or pressure. Specifically, during the course, the teacher’s effort and interest underwent changes on four occasions, characterized by four distinct dynamic patterns in terms of the interaction of high and low interest and effort. The four emerging patterns of L2 teacher effort and interest indicate that the construct of grit could be explained in terms of four dynamic clusters or archetypes. This study provides implications for understanding the complex dynamic nature of grit, which can be further explored through cluster analytic approaches in future studies.

A cluster analytic examination and validation of adult victim sexual offending subtypes in two Canadian samples

The development and validation of sexual offense perpetrator typologies remains a useful endeavor with implications for theory and correctional/clinical practice. Most such typologies—which rely on factors such as the individual’s motivation for offending—have not been validated empirically. The current study utilized a validated sexual violence risk-needs instrument, the Violence Risk Scale—Sexual Offense version (VRS-SO; Wong, Olver, Nicholaichuk, & Gordon [2003, 2017], Regional Psychiatric Centre and University of Saskatchewan, Saskatoon, Canada), to develop and validate an empirically-derived adult victim sexual offense (AVSO) typology through model-based cluster analysis of dynamic risk-need domains. The study featured two treated samples of men (n = 283 and 169) convicted for contact sexual offenses against adult victims. A three-cluster solution was identified and replicated across the two samples: high antisociality high deviance (HA-HD), high antisociality low deviance (HA-LD), and low antisociality low deviance (LA-LD). External validation analyses demonstrated that HA-HD men had more dense sexual offense histories, were more likely to be diagnosed with a paraphilia, and had the highest rates of sexual recidivism (Sample 2 only). By contrast, the HA-LD men had greater concerns on indexes of nonsexual criminality, particularly high base rates of antisocial personality and substance use disorders, and high rates of general violent recidivism (particularly Sample 1). The findings suggest that the VRS-SO factors may have utility in discriminating between AVSO types to inform sexual offending theory, case formulation, and risk management.

A review of cluster analysis techniques and their uses in library and information science research: k-means and k-medoids clustering

PurposeThis literature review explores the definitions and characteristics of cluster analysis, a machine-learning technique that is frequently implemented to identify groupings in big datasets and its applicability to library and information science (LIS) research. This overview is intended for researchers who are interested in expanding their data analysis repertory to include cluster analysis, rather than for existing experts in this area.Design/methodology/approachA review of LIS articles included in the Library and Information Source (EBSCO) database that employ cluster analysis is performed. An overview of cluster analysis in general (how it works from a statistical standpoint, and how it can be performed by researchers), the most popular cluster analysis techniques and the uses of cluster analysis in LIS is presented.FindingsThe number of LIS studies that employ a cluster analytic approach has grown from about 5 per year in the early 2000s to an average of 35 studies per year in the mid- and late-2010s. The journal Scientometrics has the most articles published within LIS that use cluster analysis (102 studies). Scientometrics is the most common subject area to employ a cluster analytic approach (152 studies). The findings of this review indicate that cluster analysis could make LIS research more accessible by providing an innovative and insightful process of knowledge discovery.Originality/valueThis review is the first to present cluster analysis as an accessible data analysis approach, specifically from an LIS perspective.