/CSC8631-Project

Estimating article reading time using Learning analytics dataset.

Primary LanguageR

About The Project

Businesses are making effort to achieve an edge with its content marketing program. Showing an article's reading time to each of article can have a profound and positive effect on reader engagement levels. The first benefit is that users to help them choose the right article for the right amount of available time they have. Another advantage is that estimated reading time impacts engagement metrics seriously. Lastly, it helps to make analyzes according to certain groups.

Reading time is calculated based on the number of words read by the average human per minute. Generally, this average value per minute is between 200 and 250 words. Therefore, the total number of words in the article is divided by this determined average.

However, scientific articles are not like blog posts. Reading time varies according to one's knowledge, age, country even article subject .For example, professionals can read articles more quickly than students or older people read slower. Hence, we are not making an accurate estimate by using the conventional calculation method.

In this study, the data will be analyzed and the article reading time will be examined in which groups and how it changes. Then, a regression model using these features will be developed that can predict article reading time on a per-person basis.

Deliverables

  • Report file => reports/report.RMD or reports/report.pdf.
  • Critical reflection report file => reports/short_report.RMD or reports/short_report.pdf.
  • Presentation => docs/Presentation.pdf or docs/Presentation.pptx.
  • Presentation video => docs/Presentation.mp4.
  • Git Log file => logs/210351491_GitLogFile.txt.

Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

  • R

  • ProjectTemplate

    install.packages('ProjectTemplate')

Installation

  1. Clone the repo

    git clone https://github.com/muzaffersenkal/CSC8631-Project

  2. Load Project

    library('ProjectTemplate')
load.project()

Usage

After you enter the second line of code, you'll see a series of automated messages as ProjectTemplate goes about doing its work. This work involves:

  • Reading in the global configuration file contained in config.
  • Loading any R packages you listed in the configuration file.
  • Datasets stored in data or cache.
  • Preprocessing the data using the files in the munge directory.
  • Reports files are in the reports directory.
  • Analysis are in the src directory.

Contributing

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

Contact

Muzaffer Senkal - email

Project Link: https://github.com/muzaffersenkal/CSC8631-Project/