Wie können Sie statistische Programmierung nutzen, um gefährdete Schüler zu identifizieren?

Analyze academic performance data using statistical models, identifying patterns to predict and intervene with students at risk of underachievement

In my experience; the effort to identify and support at-risk students is not just about improving academic performance; it's about recognizing the holistic needs of each student and ensuring they have the support and resources needed to thrive. It reflects a commitment to creating an educational system that values every student's potential and works proactively to foster a supportive and inclusive learning environment.

Statistical programming can be used to identify students at risk by analyzing relevant data and applying statistical techniques. Here are two steps : Data Collection: Gather relevant data about students, such as academic performance, attendance records, demographic information, socio-economic factors, and other relevant variables. Data Cleaning and Preparation: Preprocess the data by removing any inconsistencies, missing values, or outliers. Normalize or standardize the data if necessary.

According to the book, DROPPED by Dr. E. Marcel Jones, several variables have varying influences on the decision to drop out, including grade point average, number of failed courses, and a variety of socioeconomic ills. School district administrators and legislators need sound research and regression testing to predict dropout rates within their entities. Having this accurate knowledge would prove beneficial in establishing intervention programs, allocating resources for prevention, and implementing appropriate graduation policies. To this end, educators will be able to pinpoint the areas of need and develop effective intervention strategies that will aid in reducing the dropout rate for their districts.

Apply machine learning algorithms to build predictive models that can classify students into different risk levels. We can use supervised learning methods, such as logistic regression, decision trees, random forests, or neural networks, to train and test the models on a subset of data. We can also use unsupervised learning methods, such as clustering or principal component analysis, to discover patterns and groups in your data.

Statistical programming analyzes academic and behavioral data to identify at-risk students. It involves collecting and cleaning data, selecting predictive features, using statistical techniques to develop models, predicting risks, assessing model performance, implementing interventions, and continuously monitoring outcomes for improvement.

The goal is to use data to predict which students might face academic, social, or personal challenges that could hinder their educational progress. Using Python, one of the most popular programming languages for statistical analysis: Evaluate your model using appropriate metrics such as accuracy, precision, recall, and the AUC-ROC curve. Adjust your model as needed to improve its predictive performance.

You could consider the demographics (gender, ethnicity, socioeconomic origin, etc), academic performance (grades, test scores, attendance, etc), behaviour (participation, feedback, engagement, etc), and intrinsically motivation (autonomy, interest). But in reality the data you will focus on depends on your intentions and your scope of action: What do you want to achieve and what can you do for these students? Consider these 2 questions before collecting data and deciding what information you need. To collect the data you can use various sources: previous and actual student records, surveys, auto-evaluations before and after a course, analytics included in learning platforms or you can collect them yourself.