Student Internet Usage


Educational institutions generally provide Internet access to students and it is interesting to determine how the students use the Internet.

Project Description

This project has two customers with two corresponding areas of interest related to student use of the Internet.

Research Area 1

LTC Avery Leider wants to study our perceptions of where students are on the Internet and compare those thoughts to the actual data of what the machines tell us of where students are actually spending school Internet resources. The purpose of the study is to yield data that helps the school respond to the Internet usage in ways that optimize service in both areas: in how that service is perceived as well as in how it meets the actual demand.

Three tasks are required:

  1. We need a survey that asks students at Pace (then, later, students at other schools) to respond to, and that survey needs to yield clean straightforward data that is easy to chart and graph. Graphs are such an important part of the results, that from the first step we need to design for them. For example, if the question asks about what websites the student is on, the list is a selection list (Facebook, LinkedIn, Instagram, Other) instead of a fill-in text box that might yield data that has to be cleaned up before it can be used because of misspellings.

    The survey should be designed such that each student responding to the survey gets an interactive response as they are doing it. For example, after they respond to a question, the response might be "Your use of Facebook is below the average for your age group: according to [source], 20% of users your age use Facebook more than three times a day." This design makes it more likely that the survey will be completed and not abandoned midway, because of user curiosity.

    The survey can be on Google Forms or Survey Monkey or other format, just as long as the data received can be easily used.

  2. We need is to know at what exact place in the network at Pace University will be the data that shows where students actually go to on the Internet. The type of device. This way we know what log to ask for from the Pace ITS service, who might be requesting it from the Internet Service Provider if they don't monitor this at their gateway device.

    Once we get the data, we will need to know how to make sense of it so we can compare the data of actual Internet usage to the perceived Internet usage in a meaningful way. So we need to know what the data will look like - will it be long stretches of text with ip addresses listed? Will it be a comma separated value file?

    The project doesn't need to succeed in actually getting the data, if the design and explanations are good, it can be used to request the data. The data request will be more likely to be approved if the decision maker understands completely what the data is being used for.

  3. A first-draft of a Users Manual is needed, that can be refined and completed in future to help export this design and idea to other colleges and universities so others can easily duplicate this study in their own environments.

Research Area 2

Jigar Jadav is interested in mobile learning in a K-12 setting, where Mobile Learning can be defined as learning that is supported or delivered by a handheld or mobile device (Hutchinson, 2012). He will be obtaining K-12 student generated data on iPads.

Background: The K-12 learning space as we know is slowly changing worldwide. It is becoming more 21st century oriented where students are given access to the internet through various platforms such as desktop computers, laptops, tablets and other mobile devices. In 2000, the United States congress passed the Children’s Internet Protection Act (CIPA) law to help safeguard children connected to the internet through school computers. Schools are required to use web filters to block inappropriate content to students. Some schools are distributing iPads to students to integrate technology in the classroom. These devices have a web filter installed on them irrespective to what Wi-Fi network they’re connected to. There is a vast amount of data being collected through these web filters.

Project Description: The project will focus on big data analysis of the student generated data, using, for example, text analytics from a big data and machine learning perspective. Currently, there is no data analysis being performed on this vast amount of data mentioned above. This is a big data problem with mostly unstructured data collected from students performing various searches on the internet. The data holds the key to answering several questions:

  1. Can the searches performed by students be classified into two categories – schools related and non-school related?
  2. Is there a correlation between the total number of schoolwork related searches and non-schoolwork related searches to student GPA?
  3. Can a teacher be provided with a real-time breakdown of student’s schoolwork related searches to improve time-on-task?
  4. Can this data analysis help teachers and guidance counselors identify students that need Response to Intervention (RTI) sooner?
  5. Based on the usage history of paid apps, can schools districts make better app investment decisions in the future? (For example, should the bottom 5, least used educational apps, be dropped from next year’s budget? What makes the top 5 most used educational apps unique and can similar apps be found to replace the least used apps?)
Importance: The results of the findings could be game changing from the standpoint of student learning, teacher instruction, and district spending on technology and purchase of mobile apps for education. The use of tablet devices in the classrooms is in its infancy. However, there is a major push in recent years to move in the direction of mobile learning. Stakeholders have to make monetary decisions as to how much of a school budget should be used to facilitate this type of learning. Teachers can be provided with appropriate professional development in integrating interactive lesson plans with mobile devices. Teachers can help improve student’s time-on-task through real-time data analysis. Above all, students can be engaged in a more meaningful way through such data analysis and enhance learning experiences.

Also see Slides by Jigar Jadav.


Additional initial references will be provided by the customers.