pandas-dev/pandas: Pandas 1.2.3

BackgroundMeasurement of students' self‐regulation skills is an active topic in education research, as effective assessment helps devising support interventions to foster academic achievement. Measures based on event tracing usually require large amounts of data (e.g., MOOCs and large courses), while aptitude measures are often qualitative and need careful interpretation. Precise and interpretable evaluation of self‐regulation skills in a normal K‐12 classroom thus poses a challenge.ObjectivesThe present study proposes and explores a learning analytics method of combining aptitude and event measures to evaluate student's self‐regulation skills.MethodsAn explorative learning analytics study was conducted in a junior high school mathematics class ( students), using a three‐lesson intervention with digital learning materials. Students first assessed their self‐regulation skills with a self‐report questionnaire, after which trace logs and observations of student behaviour were collected. Learning sessions were extracted from trace logs, clustered, and linked to learning strategies. Students were clustered by the self‐report results and learning behaviour profiles. Session clusters, student behaviour clusters and assignment grades were also tested for association.Results and ConclusionsThe detected session and student behaviour types were linked to learning tactics and strategies found in prior studies. Additionally, association was found between self‐reported self‐regulation skills and the student behaviour obtained from trace logs.ImplicationsThe results demonstrate the feasibility of concurrently using aptitude and event measures on a classroom scale, providing teachers with a tool to evaluate and support self‐regulated learning. Combined with further measures like predictive learning analytics, teachers can obtain an early and highly interpretable picture of at‐risk students in their classes.

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“…Data analysis was completed using Pandas (Reback et al, 2021), PM4Py (Berti et al, 2019) and scikit-learn (Pedregosa et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Zhidkikh

Saarela

Kärkkäinen

2023

Computer Assisted Learning

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“…The majority of analyses were conducted using Python 3.8.10 (Van Rossum & Drake, 2009) and the following packages: Scikit-learn 0.21.1 (Pedregosa et al, 2011), pandas 1.2.3 (Reback et al, 2021), NumPy 1.19.5 (Harris et al, 2020), and sciPy 1.6.0 (Virtanen et al, 2020). Differential prediction standardized effect sizes were calculated in R 3.6.0 (R Core Team, 2022) using the psychmeta package (Dahlke & Wiernik, 2019).…”

Section: Methodsmentioning

confidence: 99%

New strategies for addressing the diversity–validity dilemma with big data.

Rottman¹,

Gardner²,

Liff³

et al. 2023

Journal of Applied Psychology

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The diversity–validity dilemma is one of the enduring challenges in personnel selection. Technological advances and new techniques for analyzing data within the fields of machine learning and industrial organizational psychology, however, are opening up innovative ways of addressing this dilemma. Given these rapid advances, we first present a framework unifying analytical methods commonly used in these two fields to reduce group differences. We then propose and demonstrate the effectiveness of two approaches for reducing group differences while maintaining validity, which are highly applicable to numerous big data scenarios: iterative predictor removal and multipenalty optimization. Iterative predictor removal is a technique where predictors are removed from the data set if they simultaneously contribute to higher group differences and lower predictive validity. Multipenalty optimization is a new analytical technique that models the diversity–validity trade-off by adding a group difference penalty to the model optimization. Both techniques were tested on a field sample of asynchronous video interviews. Although both techniques effectively decreased group differences while maintaining predictive validity, multipenalty optimization outperformed iterative predictor removal. Strengths and weaknesses of these two analytical techniques are also discussed along with future research directions.

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“…For the Scopus search, it was performed using the same keyword. Both results were visualized together using Bokeh and NumPy libraries in Python. − …”

Section: Methodsmentioning

confidence: 99%

Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection

et al. 2021

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Droplet microfluidics has revealed innovative strategies in biology and chemistry. This advancement has delivered novel quantification methods, such as droplet digital polymerase chain reaction (ddPCR) and an antibiotic heteroresistance analysis tool. For droplet analysis, researchers often use image-based detection techniques. Unfortunately, the analysis of images may require specific tools or programming skills to produce the expected results. In order to address the issue, we explore the potential use of standalone freely available software to perform image-based droplet detection. We select the four most popular software and classify them into rule-based and machine learning-based types after assessing the software’s modules. We test and evaluate the software’s (i) ability to detect droplets, (ii) accuracy and precision, and (iii) overall components and supporting material. In our experimental setting, we find that the rule-based type of software is better suited for image-based droplet detection. The rule-based type of software also has a simpler workflow or pipeline, especially aimed for non-experienced users. In our case, CellProfiler (CP) offers the most user-friendly experience for both single image and batch processing analyses.

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pandas-dev/pandas: Pandas 1.2.3

Cited by 22 publications

References 0 publications

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

New strategies for addressing the diversity–validity dilemma with big data.

Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection

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