Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

As we enter the Post-Human age many of our beliefs become redundant—not least the belief in human being.

What is a Human

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

https://dl.acm.org/doi/pdf/10.1145/3293881.3295783

Predicting academic performance: a systematic literature review

Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of e ducational datasets further catalyse this interestingness, especially in online learning. Although several EDM surveys are available in the literature, we could find only a few specific surveys on student performance analysis and prediction. These specific surveys are limited in nature and primarily focus on studies that try to identify possible predictor or model student performance. However, the previous works do not address the temporal aspect of prediction. Moreover, we could not find any such specific survey which focuses only on classroom-based education. In this paper, we present a systematic review of EDM studies on student performance in classroom learning. It focuses on identifying the predictors, methods used for such identification, time and aim of prediction. It is significantly the first systematic survey of EDM studies that consider only classroom learning and focuses on the temporal aspect as well. This paper presents a review of 140 studies in this area. The meta-analysis indicates that the researchers achieve significant prediction efficiency during the tenure of the course. However, performance prediction before course commencement needs special attention.

Student performance analysis and prediction in classroom learning: A review of educational data mining studies

We move beyond "Is Machine Learning Useful for Macroeconomic Forecasting?" by adding the "how". The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. In contrast, we study the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish four so-called features (nonlinearities, regularization, cross-validation and alternative loss function) and study their behavior in both the data-rich and data-poor environments. To do so, we design experiments that allow to identify the "treatment" effects of interest. We conclude that (i) nonlinearity is the true game changer for macroeconomic prediction, (ii) the standard factor model remains the best regularization, (iii) K-fold cross-validation is the best practice and (iv) the $L_2$ is preferred to the $\bar \epsilon$-insensitive in-sample loss. The forecasting gains of nonlinear techniques are associated with high macroeconomic uncertainty, financial stress and housing bubble bursts. This suggests that Machine Learning is useful for macroeconomic forecasting by mostly capturing important nonlinearities that arise in the context of uncertainty and financial frictions.

How is Machine Learning Useful for Macroeconomic Forecasting

Machine Learning (ML) requires a certain number of features (i.e., attributes) to train the model. One of the main challenges is to determine the right number and the type of such features out of the given dataset’s attributes. It is not uncommon for the ML process to use dataset of available features without computing the predictive value of each. Such an approach makes the process vulnerable to overfit, predictive errors, bias, and poor generalization. Each feature in the dataset has either a unique predictive value, redundant, or irrelevant value. However, the key to better accuracy and fitting for ML is to identify the optimum set (i.e., grouping) of the right feature set with the finest matching of the feature’s value. This paper proposes a novel approach to enhance the Feature Engineering and Selection (eFES) Optimization process in ML. eFES is built using a unique scheme to regulate error bounds and parallelize the addition and removal of a feature during training. eFES also invents local gain (LG) and global gain (GG) functions using 3D visualizing techniques to assist the feature grouping function (FGF). FGF scores and optimizes the participating feature, so the ML process can evolve into deciding which features to accept or reject for improved generalization of the model. To support the proposed model, this paper presents mathematical models, illustrations, algorithms, and experimental results. Miscellaneous datasets are used to validate the model building process in Python, C#, and R languages. Results show the promising state of eFES as compared to the traditional feature selection process.

/pdf/proposing-enhanced-feature-engineering-and-a-selection-model-4lc8w43qx6.pdf

Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes

The research progress presented in this paper comes under the areas of data science. The authors propose enhanced machine learning (supervised learning) framework for the prediction of the students through stochastic probability-based math constructs/model and an algorithm [Good Fit Student (GFS)], along with the enhanced quantification of target variables and algorithmic metrics. Academia in today’s modern world sees the problem of dropouts, low retention, poor student performances, lack of motivation, and unnecessary change of study majors and re-admissions. The authors consider this challenge as a research problem and attempt to solve it by utilizing social networking-based personality traits, relevant data and features to improve the predictive modeling approach. The authors recognize that admission choices are often governed by family trends, affordability, basic motivation, market trends, and natural instincts. However, natural gifts and talents are minimally used to select such directions in the academics. The authors based on literature review identify this a research gap and improves with a unique blend of algorithms/methods, an improved modeling of performance metrics, built upon cross-validation to improve fitness, and enhance the process of feature engineering and tuning for reduced errors and optimum fitness, at the end. The authors present the latest progress of their research in this paper. The included results show the progress of the work and ongoing improvements. The authors use machine learning techniques, Microsoft SQL Server, Excel data mining, R and Python to develop and test their model. The authors provide related work and conclude with final remarks and future work.

Proposing stochastic probability-based math model and algorithms utilizing social networking and academic data for good fit students prediction

Machine learning (ML) has shown enormous potential in various domains with the wide variations of underlying data types. Because of the miscellany in the data sets and the features, ML classifiers often suffer from challenges, such as feature miss-classification, unfit algorithms, low accuracy, overfitting, underfitting, extreme bias, and high predictive errors. Through the lens of related study and latest progress in the field, this paper presents a novel scheme to construct logical table (LT) unit with two internal sub-modules for algorithm blend and feature engineering. The LT unit works in the deepest layer of an enhanced ML engine engineering (eMLEE) process. eMLEE consists of several low-level modules to enhance the ML classifier progression. A unique engineering approach is adopted in eMLEE to blend various algorithms, enhance the feature engineering, construct a weighted performance metric, and augment the validation process. The LT is an in-memory logical component, that governs the progress of eMLEE, regulates the model metrics, improves the parallelism, and keep tracks of each module of eMLEE as the classifier learns. Optimum fitness of the model with parallel “check, validate, insert, delete, and update” mechanism in 3-D logical space via structured schemas in the LT is obtained. The LT unit is developed in Python, C#, and R libraries and tested using miscellaneous data sets. Results are created using GraphPad Prism, SigmaPlot, Plotly, and MS Excel software. To support the built and implementation of the proposed scheme, complete mathematical models along with the algorithms, and necessary illustrations are provided in this paper. To show the practicality of the proposed scheme, several simulation results are presented with a comprehensive analysis of the outcomes for the metrics of the model that the LT regulates with improved outcomes.

Proposing Logical Table Constructs for Enhanced Machine Learning Process

A tremendous growth and progress has shown the potential of big data (i.e structured, unstructured and semi-structured) to extract valuable information and do reliable prediction for several industries. Social networking data has created additional opportunities for data scientists and researchers to utilize the data points to advance the predictive and mining models and techniques. However, predictive analysis in field of academics is at its infancy. In this paper, we present a framework to implement a recommender system to improve academic choice process for new students. The framework is based on our ongoing research for Predicting Educational Relevance For an Efficient Classification of Talent (PERFECT Algorithm Engine), that utilizes stochastic probability distribution based modeling. We present an algorithm and math construct to support our work along with providing graphical results for various parameters that help the recommendation and decision process for individuals. We show related study and conclude with future work.

Recommender System Framework for Academic Choices: Personality Based Recommendation Engine (PBRE)

This paper discusses part one of the main work in field of data science, mining and analytics. Family of algorithms is developed to predict the educational relevance of individuals' talents through lens of personality features (unstructured and semi-structured) and academic/career data. The big data (unstructured and semi-structured) contains lots of valuable information that can be mined and analyzed. However, such processing and utilization of data introduce challenges of dealing with noise (irrelevant, unnecessary and redundant data). Regardless of the nature of data processing and utilization for a given problem or an application, noise adds unnecessary time and cost. This paper briefly discusses the overall research work and then presents Noise Removal and Structured Data Detection (NR-and-SDD) algorithm and related math construct. NR-and-SDD detects the noise to reduce the processing cost and improve structured data detection in relevance of personality features. The given results show improved reliability and efficiency of NR and SDD processes. Related study is provided and paper is concluded with final remarks and future works.

Muhammad Fahim Uddin

Papers

Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes

Proposing stochastic probability-based math model and algorithms utilizing social networking and academic data for good fit students prediction

Proposing Logical Table Constructs for Enhanced Machine Learning Process

Recommender System Framework for Academic Choices: Personality Based Recommendation Engine (PBRE)

Noise removal and structured data detection to improve search for personality features