Digital Teaching Assistant for the Python Programming Course

Sovietov, Peter N.; Gorchakov, Artyom V.

doi:10.1109/tele55498.2022.9801060

Cited by 7 publications

(22 citation statements)

References 5 publications

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“…In the research presented in this paper, we consider the classification problem of program texts solving automatically generated programming exercises of 11 different types (see Table 1). The programming exercises were generated by the Digital Teaching Assistant (DTA) system [9,10], which automates the massive Python programming course at MIREA-Russian Technological University. The solutions for the exercises were submitted to DTA by students using a web-based user interface [9].…”

Section: Discussionmentioning

confidence: 99%

“…The Digital Teaching Assistant system automates the massive Python programming course at MIREA-Russian Technological University. DTA automatically generates [6] unique programming exercises of 11 different types and automatically checks the programs submitted by students [9,10]. Brief descriptions of programming exercise types generated by DTA are listed in Table 1.…”

Section: Digital Teaching Assistantmentioning

confidence: 99%

“…Some examples of formulations for unique tasks generated automatically by the DTA system are shown in Figure 1. Every program submitted by a student to DTA via a web-based UI is stored in an immutable table of a relational database, and the submissions are later checked one by one automatically in a background process [9]. By the end of semester, teachers employ clustering techniques available in the DTA analytics module in order to automate submitted source code analysis, with an intention to identify approaches commonly used by students while solving automatically generated unique programming tasks.…”

Section: Digital Teaching Assistantmentioning

confidence: 99%

“…Such metrics as ( 8), (9), and ( 10) are useful when searching for a classifier without bias towards classes that occur in a dataset most frequently.…”

Section: Multi-class Classification Metricsmentioning

confidence: 99%

“…The Digital Teaching Assistant system (DTA) [9,10], which automates the Python programming course at MIREA-Russian Technological University, offers means for Python programming tasks generation of various types, as well as means for automatic assessment of student submissions. The DTA system consists of three modules, namely: the core module responsible for unique programming tasks generation for every student using the generate and test approach [6], and which is also responsible for submitted solutions checking; the web application module, allowing students to interact with the core module via a web-based user interface (UI) [9]; the analytics module, allowing programming instructors to track progress statistics of every student and to analyze the large number of submitted programs with the help of machine learning.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines

Demidova

Gorchakov

2022

Algorithms

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The massive nature of modern university programming courses increases the burden on academic workers. The Digital Teaching Assistant (DTA) system addresses this issue by automating unique programming exercise generation and checking, and provides means for analyzing programs received from students by the end of semester. In this paper, we propose a machine learning-based approach to the classification of student programs represented as Markov chains. The proposed approach enables real-time student submissions analysis in the DTA system. We compare the performance of different multi-class classification algorithms, such as support vector machine (SVM), the k nearest neighbors (KNN) algorithm, random forest (RF), and extreme learning machine (ELM). ELM is a single-hidden layer feedforward network (SLFN) learning scheme that drastically speeds up the SLFN training process. This is achieved by randomly initializing weights of connections among input and hidden neurons, and explicitly computing weights of connections among hidden and output neurons. The experimental results show that ELM is the most computationally efficient algorithm among the considered ones. In addition, we apply biology-inspired algorithms to ELM input weights fine-tuning in order to further improve the generalization capabilities of this algorithm. The obtained results show that ELMs fine-tuned with biology-inspired algorithms achieve the best accuracy on test data in most of the considered problems.

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Section: Discussionmentioning

confidence: 99%

Section: Digital Teaching Assistantmentioning

confidence: 99%

Section: Digital Teaching Assistantmentioning

confidence: 99%

“…Such metrics as ( 8), (9), and ( 10) are useful when searching for a classifier without bias towards classes that occur in a dataset most frequently.…”

Section: Multi-class Classification Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines

Demidova

Gorchakov

2022

Algorithms

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Algorithm for Detecting Anomalous Student Activities in the Online Learning Process Based on Box Plots

Demidova,

Misailidi

2023

2023 5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA)

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Multi-Class Text Classification on Khmer News Using Ensemble Method in Machine Learning Algorithms

Phann¹,

Soomlek²,

Seresangtakul³

2023

AIP

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The research herein applies text classification with which to categorize Khmer news articles. News articles were collected from three online websites through web scraping and grouped into nine categories. After text preprocessing, the dataset was split into training and testing sets. We then evaluated the performance of the ensemble learning method via machine learning classifiers with kfold validation. Various machine learning classifiers were employed, namely logistic regression, Complement Naive Bayes, Bernoulli Naive Bayes, k-nearest neighbours, perceptron, support vector machines, stochastic gradient descent, AdaBoost, decision tree, and random forest were employed. Accuracy was improved for the categorization of Khmer news articles, in which Grid Search CV was used to find the optimal hyperparameters for each machine learning classifier with feature extraction TF-IDF and Delta TF-IDF. The results determined that the highest accuracy was achieved through the ensemble learning method in the support vector machine with the optimal hyperparameters (C = 10, kernel = rbf), using feature extraction TF-IDF and Delta TF-IDF, at 83.47% and 83.40%, respectively. The model establishes that Khmer news articles can be accurately categorized.

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Digital Teaching Assistant for the Python Programming Course

Cited by 7 publications

References 5 publications

Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines

Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines

Algorithm for Detecting Anomalous Student Activities in the Online Learning Process Based on Box Plots

Multi-Class Text Classification on Khmer News Using Ensemble Method in Machine Learning Algorithms

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