Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

Browse

Filters

2023 - 20262

Settings

Author: search.filters.author.Yurum, Ozan RasitScopus Q: search.filters.scopusQuartile.Q1

Search Results

Now showing 1 - 2 of 2
  • Article
    An Alternative Software Benchmarking Dataset: Effort Estimation With Machine Learning
    (Elsevier Science Inc, 2026) Yurum, Ozan Rasit; Unlu, Huseyin; Demirors, Onur
    Effort estimation plays a vital role in software project planning, as accurate estimates of required human resources are essential for success. Traditional estimation models often depend on historical size and effort data, yet organizations frequently struggle to access reliable effort records. Public benchmarking datasets like ISBSG offer useful data but may lack coverage or involve licensing fees. To address this issue, we previously introduced a free, extendable benchmarking dataset that integrates functional size and effort data extracted from 18 studies. In this study, we examine the effectiveness of our dataset for predictive effort estimation and compare it with the widely used ISBSG dataset. Our analysis includes 337 records from our dataset and 732 ISBSG projects, focusing on those with COSMIC size data. We first developed and compared models using linear regression and nine machine learning algorithms - Bayesian Ridge, Ridge Regression, Decision Tree, Random Forest, XGBoost, LightGBM, k-Nearest Neighbors, Multi-Layer Perceptron, and Support Vector Regression. Then, we selected the best-performing models and applied them to an unseen evaluation dataset to assess their generalization performance. The results show that machine learning performance varies based on evaluation method and dataset characteristics. Despite having fewer records, our dataset enabled more accurate predictions than ISBSG in most cases, highlighting its potential for effort estimation. This study demonstrates the viability of our dataset for building predictive models and supports the use of machine learning in improving estimation accuracy. Expanding this dataset could offer a valuable, open-access resource for organizations seeking effective and lowcost estimation solutions.
  • Review
    Citation - WoS: 3
    Citation - Scopus: 4
    Predictive Video Analytics in Online Courses: a Systematic Literature Review
    (SPRINGER, 2023) Yurum, Ozan Rasit; Taskaya-Temizel, Tuğba; Yildirim, Soner
    The purpose of this study was to investigate the use of predictive video analytics in online courses in the literature. A systematic literature review was performed based on a hybrid search strategy that included both database searching and backward snowballing. In total, 77 related publications published between 2011 and April 2023 were identified. The findings revealed an increase in the number of publications on predictive video analytics since 2016. In the majority of studies, edX and Coursera platforms were used to collect learners' video interaction data. In addition, computer science was shown to be the top course domain, whilst data collection from a single course was found to be the most common. The results related to input measures showed that pause, play, backward, and forward were the top in-video interactions, whilst video transcript and subtitle were the least used. Learner performance and dropout were the primary output measures, whereas learning variables such as engagement, satisfaction, and motivation were investigated in only a few studies. Furthermore, most of the studies utilized data related to forums, navigation, and exams in addition to video data. The top algorithms used were Support Vector Machine, Random Forest, Logistic Regression, and Recurrent Neural Networks, with Random Forest and Recurrent Neural Networks being two rising algorithms in recent years. The top three evaluation metrics used were Accuracy, Area Under the Curve, and F1 Score. The findings of this study may be used to aid effective learning design and guide future research.