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AbstractArticles published in 22 counseling journals from 2010–2019 were compared on 26 author and article characteristics. Women now constitute majorities of authorship and counseling journals are publishing larger proportions of research articles, 63% of which used quantitative approaches and about 11% of which studied counseling interventions.

This study presents the results of a professor's semester-long experience of teaching a data wrangling course to a Master of Business Administration (MBA) class where most students had little or no background in coding or computing. The goal of this study was to better understand students' perceptions using DataCamp to learn data wrangling concepts. Voluntary, self-reported data were collected at the end of learning modules throughout the semester to evaluate their learning experience using DataCamp to learn data wrangling concepts. One major finding of this study is that the majority of students were surprised at how long it took them to complete more real-world problems in RStudio, given their previous experience with DataCamp. This finding speaks to some of the limitations of DataCamp in delivering the rich, complex, data-centered experiences that students need. This finding suggests that more open-ended problem-solving assignments can push students beyond what they learn from DataCamp alone.

This study explored the perceptions and experiences of content creator students enrolled in an undergraduate course in lifespan development. A total of 284 students participated in this study over five semesters. A mixed-methods study design was adopted for data collection, and a thematic analysis of qualitative data was conducted to identify the key themes. The participants identified several themes about why they liked creating content were identified, such as seeing other students' perspectives and autonomy over the learning process, along with several negative aspects of this active learning approach, including having to summarise everyone's responses and unclear directions. These findings can guide other instructors to incorporate active learning online assignments into their existing courses.

Modern technologies have allowed for the amassment of data at a rate never encountered before. Organizations are now able to routinely collect and process massive volumes of data. A plethora of regularly collected information can be ordered using an appropriate time interval. The data would thus be developed into a time series. Time series data mining methodology identifies commonalities between sets of time-ordered data. Time series data mining detects similar time series using a technique known as dynamic time warping (DTW). This research provides a practical application of time series data mining. A real-world data set was provided to the authors by dunnhumby. A time series data mining analysis is performed using retail grocery store chain data and results are provided.

Reject inference is one of the key processes required to build relevant credit scorecard models. Reject inference is used to infer the good or bad loan status to credit applicants that were rejected by the financial institution. If rejected applicants data is not used in the updating of the credit scoring model, the model is biased because it will not be representative of the entire applicant population. Many reject inference techniques perform an extrapolation method to infer the good or bad loan status of the rejected applicants. The issues with extrapolation are discussed, and this study provides a novel reject inference technique in which the rejected applicants are included in the model estimation process. The extrapolation problem is avoided using the methodology in this paper. The newly proposed reject inference technique is shown to outperform the standard extrapolation technique using a simulation study.