Weighted word embeddings and clustering‐based identification of question topics in MOOC discussion forum posts

Onan, Aytuğ; Toçoğlu, Mansur Alp

doi:10.1002/cae.22252

Cited by 38 publications

(23 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Text classification by simple RF is the goal of [ 84 ]. Onan and Toçouglu [ 85 ] present a method for document clustering and topic modeling on massive open online courses. Sentiment analysis of technical words in English by the Gini index for feature selection is done by [ 86 ].…”

Section: Related Workmentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

show abstract

Section: Related Workmentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Bao et al [20] proposed a SC-Net using semantic coherence for machine-generated spam text detection. In addition, some methods based on data mining and analysis [21][22][23], weighted word embedding [24][25][26], keyword extraction [27], and machine learning [28,29] have been proposed.…”

Section: Related Workmentioning

confidence: 99%

Self-Information Loss Compensation Learning for Machine-Generated Text Detection

Wang

Feng

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The technology of automatic text generation by machine has always been an important task in natural language processing, but the low-quality text generated by the machine seriously affects the user experience due to poor readability and fuzzy effective information. The machine-generated text detection method based on traditional machine learning relies on a large number of artificial features with detection rules. The general method of text classification based on deep learning tends to the orientation of text topics, but logical information between texts sequences is not well utilized. For this problem, we propose an end-to-end model which uses the text sequences self-information to compensate for the information loss in the modeling process, to learn the logical information between the text sequences for machine-generated text detection. This is a text classification task. We experiment on a Chinese question and answer the dataset collected from a biomedical social media, which includes human-written text and machine-generated text. The result shows that our method is effective and exceeds most baseline models.

show abstract

“…e results revealed that teacher, content, assessment, and schedule had significant effects on student satisfaction. Onan et al developed a document-clustering model based on weighted word embedding to identify question topics on posts of MOOC forum [12]. Later, Onan designed a long short-term memory networks (LSTM) to classify the sentiment for about 70,000 MOOC reviews, which can achieve a high classification accuracy [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Learning Behavior Using Log Data in Blended Teaching

Xie

Qiong

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

Online and offline blended teaching mode, the future trend of higher education, has recently been widely used in colleges around the globe. In the article, we conducted a study on students’ learning behavior analysis and student performance prediction based on the data about students’ behavior logs in three consecutive years of blended teaching in a college’s “Java Language Programming” course. Firstly, the data from diverse platforms such as MOOC, Rain Classroom, PTA, and cnBlog are integrated and preprocessed. Secondly, a novel multiclass classification framework, combining the genetic algorithm (GA) and the error correcting output codes (ECOC) method, is developed to predict the grade levels of students. In the framework, GA is designed to realize both the feature selection and binary classifier selection to fit the ECOC models. Finally, key factors affecting grades are identified in line with the optimal subset of features selected by GA, which can be analyzed for teaching significance. The results show that the multiclass classification algorithm designed in this article can effectively predict grades compared with other algorithms. In addition, the selected subset of features corresponding to learning behaviors is pedagogically instructive.

show abstract

Weighted word embeddings and clustering‐based identification of question topics in MOOC discussion forum posts

Cited by 38 publications

References 39 publications

A New Random Forest Algorithm Based on Learning Automata

A New Random Forest Algorithm Based on Learning Automata

Self-Information Loss Compensation Learning for Machine-Generated Text Detection

Predicting Learning Behavior Using Log Data in Blended Teaching

Contact Info

Product

Resources

About