Universities Tracking Students: Methods And Implications Explored

Universities are increasingly turning to technology to track their students, with some institutions using surveillance systems to monitor academic performance, attendance, and mental health. This data is then used to predict whether students will struggle with their courses or drop out. While some universities enforce strict attendance policies, others leave it up to the students to decide whether to attend class. Various methods are used to track attendance, including old-school roll calls, mobile apps, and even fingerprint scanners. Critics of these tracking methods have raised concerns about the impact on student privacy, with some students feeling that continuous monitoring is reminiscent of Big Brother.

Attendance tracking

Traditional Methods

Roll calls, or calling out student names, is a straightforward and inexpensive approach. However, it can be time-consuming, especially for larger classes, and may not be entirely reliable. Similarly, passing around a paper register or sign-in sheet for students to mark their attendance is another traditional method. This approach is usually reserved for smaller classes due to its time-consuming nature and potential waste of paper.

Card Swipe Systems

Many universities implement card swipe systems, where students use their student IDs to scan into lecture theatres or seminar rooms. This method provides a quick and easy way to electronically record attendance. However, a potential drawback is that students could scan cards for peers who are absent, which is considered a breach of conduct and can lead to disciplinary action.

QR Codes

QR codes displayed by instructors allow students to quickly scan and mark their attendance. While this method is faster than traditional roll calls, it has reliability issues. Students can easily share QR codes or send photos to absent peers, leading to inaccurate attendance records.

Mobile Applications

With the widespread use of smartphones, universities are increasingly adopting mobile apps for attendance tracking. These apps utilise Bluetooth and location services to detect the presence of students in the required location. For example, the Attendance Radar app offers a robust and affordable solution, providing free and premium versions to cater to different university needs.

Fingerprint Scanners

Fingerprint scanners provide a reliable and efficient way to track attendance. Students scan their fingerprints when entering the classroom, and the system automatically registers their attendance. However, this method can be costly to set up and maintain, particularly for larger universities.

Other Technological Methods

In addition to the methods mentioned above, universities may also explore other technological advancements, such as facial recognition, geofencing, or Bluetooth-only solutions. These methods vary in terms of reliability, ease of use, and cost, with concerns around data privacy and accuracy being important considerations.

Student data collection

Universities are increasingly adopting learning analytics to improve learning and reduce dropout rates. This involves collecting and analysing student data, such as attendance records, academic performance, and online resource usage patterns. While this practice can be beneficial, it has also raised concerns about student privacy and ethical implications.

Methods of Data Collection

Universities employ various methods to collect student data. Traditional approaches include roll calls and paper sign-in sheets, but these are time-consuming and unreliable, especially for large classes. More modern techniques, such as QR codes, have been introduced but come with their own limitations, as they can be easily shared among students, leading to inaccurate records.

The use of mobile applications, particularly those utilising Bluetooth technology, has emerged as a popular solution. For example, the Attendance Radar app provides professors with an accessible, reliable, and cost-effective way to track attendance. Similarly, universities like Syracuse University have implemented apps that monitor student attendance by connecting with Bluetooth beacons installed in lecture halls.

Data Usage

The data collected by universities serves multiple purposes. One of the primary objectives is to enforce attendance policies, which often require students to attend a certain percentage of classes to pass a course. Additionally, universities use the data for research purposes, such as understanding the correlation between attendance and academic performance, and measuring student engagement.

In recent years, universities have also started using predictive analytics to identify students who might be at risk of dropping out or struggling academically. By analysing historical data, universities can predict trends and patterns to provide support to students before they encounter difficulties. This approach has been credited with boosting graduation rates at universities like Georgia State University.

Ethical Considerations

While the use of student data can provide benefits, it has also sparked concerns about privacy and ethical implications. Critics argue that continuous monitoring of students' activities and locations can be invasive and reminiscent of "Big Brother" surveillance. Additionally, there are worries about the accuracy of the data and the potential for it to reinforce historical inequities or negatively impact certain student groups.

Furthermore, students often have limited awareness of the extent of data collection and tracking. Many universities do not provide transparent disclosures about their data collection practices, and students may unknowingly give consent by accepting privacy policies without fully understanding the implications.

Striking a Balance

Despite the concerns, advocates of learning analytics believe that it can provide a more personalised learning experience. Dr Bart Rienties, director of the learning analytics programme at the Open University, emphasises the importance of finding a balance between providing good instructional support and avoiding overwhelming students. Ultimately, while student data collection can offer benefits, universities must carefully navigate the potential pitfalls and ensure transparency and ethical handling of student data.

Student monitoring

Universities and colleges have been criticised for their use of surveillance technology to monitor students. In recent years, educational institutions have turned to data analytics to track students' behaviour, attendance, and performance. While some universities do not track attendance, most do, and there are various methods to do so.

Methods of Tracking Attendance

Old-school methods such as roll calls and passing a paper around are still used, but they are time-consuming and unreliable, especially in large classes. QR codes are faster but can be shared among students, leading to inaccurate records. Fingerprint scanners are reliable but expensive to set up and maintain.

Mobile apps, particularly those using Bluetooth, are becoming an increasingly popular way to monitor attendance. Apps such as SpotterEDU and Attendance Radar are used by thousands of professors globally to track attendance. These apps connect with Bluetooth beacons installed in classrooms and log attendance data.

Tracking Student Behaviour

Universities are also tracking student behaviour through their smartphones. When students enter certain classrooms, Bluetooth beacons can log whether they have attended class, if they were late, or if they left early. This data is sent back to the college, and Wi-Fi access points can continue to track students' locations around campus. This can result in thousands of location data points being collected on a single student each day.

Predictive Analytics

Some universities are using "big data" to predict students' likelihood of dropping out. This involves collecting and analysing large amounts of historical data to predict future trends. For example, Georgia State University in Atlanta uses predictive analytics to identify students who are at risk of dropping out and provides them with support. The university claims that its use of predictive analytics has led to an increase in graduation rates.

However, critics argue that this practice may invade students' privacy and reinforce racial inequities. There are concerns that algorithms could be reinforcing historical biases and disproportionately flagging Black and Latino students as high risk. Additionally, some students may be discouraged from continuing their studies if they are made aware that they have been identified as at-risk.

Learning Analytics

"Learning analytics" is another practice used by universities to predict whether students will struggle with their courses or drop out. This involves collecting and analysing data from students' everyday activities, such as swiping into the library or logging into a computer. This data can be used to provide a more personalised learning experience and improve student retention.

However, critics argue that this practice may violate students' privacy and that data collection should be opt-in rather than opt-out. There are concerns about the potential for data breaches and the sharing of data with third parties without students' knowledge or consent.

Student privacy

The use of technology to track students has raised concerns about student privacy. While universities aim to enhance the academic experience and provide support, critics question the impact on privacy and the potential for overreach.

Data Collection

Universities collect and use student data for various purposes, including admissions, enrollment, and educational technologies. This data includes sensitive information such as social security numbers, credit card details, family members' names, and addresses. While federal and state laws protect student data, there are concerns about the extent of data collection and the potential for unauthorized access or misuse.

Learning Analytics

"Learning analytics" refers to the practice of collecting and analyzing data from students' university activities, such as swiping into the library or logging into a computer. Universities use this data to predict students' academic performance and the likelihood of dropping out. While this data can be used to provide personalized support, some students have expressed concerns about continuous monitoring, likening it to "Big Brother".

Mobile Phone Tracking

Some universities have implemented systems that use Bluetooth beacons and Wi-Fi access points to track students' locations on campus. This technology can log attendance, tardiness, and movement around campus. While universities argue that this data can be used to monitor academic performance and mental health, critics argue that it invades students' privacy and may not be accurate in assessing students' well-being.

Legal Protections

In the United States, student privacy is protected by the Family Educational Rights and Privacy Act (FERPA), also known as the "Buckley Amendment". FERPA gives students the right to inspect and review their education records, request amendments, and consent to the disclosure of personally identifiable information. Other laws, such as the Protection of Pupil Rights Amendment (PPRA) and the Children's Online Privacy Protection Rule (COPPA), also protect student data.

Recommendations for Universities

To respect student privacy, universities should obtain informed consent from students before collecting and using their data. Universities should also be transparent about the data they collect, how it is used, and how it is protected. Students should have the right to opt out of data collection and tracking if they choose. Additionally, universities should ensure that only authorized individuals have access to student data and that it is securely stored to prevent data breaches.

Student success

Tracking student success is a complex and evolving process that involves multiple stakeholders and methodologies. Here are some key insights and strategies for effectively tracking student success:

Understanding Individual Students

At the core of tracking student success is understanding each student as an individual with unique needs, strengths, and challenges. This involves going beyond cognitive factors such as grades and test scores to assess non-cognitive factors like resilience, motivation, and confidence. By understanding these non-cognitive skills, institutions can better support students in their academic journey and ensure that the subjects they choose align with their personalities and interests.

Predictive Analytics and Risk Assessment

Many universities are turning to big data and predictive analytics to identify students who might be at risk of struggling academically or dropping out. This involves analysing historical data to predict future outcomes and provide early interventions. While this approach has been successful in boosting graduation rates, it has also raised concerns about privacy invasion and the potential reinforcement of racial and socioeconomic inequities.

Co-curricular and Classroom Engagement

Tracking students' engagement both inside and outside the classroom is crucial. Research shows that students who are actively engaged in extracurricular activities and connected to the social fabric of the institution are more likely to persist and succeed academically. Therefore, universities should encourage and monitor participation in co-curricular activities and track classroom engagement through assignments, attendance, and interaction with faculty.

Campus Resource Usage

Universities invest significant resources in various support services, such as academic advising, financial aid, tutoring, and student engagement centres. Tracking how students utilise these resources is essential to ensure that they are accessible, effective, and making a positive impact on student success.

Student Feedback and Surveys

Regular surveys and assessments can provide valuable insights into the student experience. Asking students about their satisfaction with their chosen major, their ability to follow the curriculum, and any challenges they face can help universities identify areas for improvement and provide targeted support.

Faculty Alerts and Communication

Faculty members and staff often have close interactions with students and can identify those who may be struggling. Effective communication among staff is crucial to identify these students early and offer appropriate interventions without overwhelming them.

Alumni Success and Outcomes

Tracking student success should not end when they graduate. Universities should maintain connections with alumni to evaluate their long-term outcomes, including further education, employment rates, and involvement with the university. This data can provide a comprehensive understanding of the impact of the university on its students and the effectiveness of its programmes.

In conclusion, tracking student success requires a multi-faceted approach that considers both academic and non-academic factors. By utilising these strategies, universities can better support their students, improve retention rates, and ultimately enhance the overall success and well-being of their student population.

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