Proposal

                        Investigating student study behavior and the effectiveness of study tools

Darryan Torres
Department: Computer Science

Proposal:

Time management and strong studying strategies are essential for success when it comes to test taking and overall course work, especially when a student is required to balance school, a job and personal issues. While there are many digital planners, and scheduling tools readily available, many find it to be difficult to follow and stay consistent with study routines. Many of these tools also lack the ability to adapt and do not account for other important factors that can be different per student such as the person’s individual learning behavior, procrastination patterns, and their work loads.

Research in cognitive psychology showed that a structured and intentful study habit improved learning outcomes(Dunlosky). Also, studies in human and computer interactions shows that systems that involve user behavior data can improve productivity and engagement (Skarlatidou). Self regulated learning theory also shows that it is important to plan, monitor, and adapt learning strategies. With improvements to software development and designs in algorithms, there would be a great opportunity to make a smart study planner that would adapt based on the user’s inputs and behavior patterns.

This study aims to find how students manage their study time, the challenges they face, and how they perceive and use readily available scheduling and study tools.

Methodology:

This study will use survey based research design to get data on student study habits and how they perceive and use scheduling and study tools. Participants:

A total of 20 participants that are undergraduate students at UCF will be randomly chosen through announcements on flyers, group chats, and reaching out to classmates.

Data collection:

Data will be collected through an anonymous online survey through google forms which will hold questions such as

Questions regarding study habits(time spent studying, and scheduling methods)

Self-stated procrastination habits

Experience with scheduling tools

Perception on systems in the scheduling tools

Open ended questions regarding effectiveness and quality of these tools

Procedure:

Recruit participants over 5 days

Collect responses over 4 days

Make sure everything is anonymous, and voluntary

Data analysis:

Data will be analyzed in averages, and percentages as well as through common themes in study challenges and tool effectiveness

Outcomes

This study will find:

Data on common study habits and time management strategies students use

Show the challenges students face when trying to have a consistent study routine

How students perceive scheduling tools

Recommendations for how to improve study planning tools

                                                Works Cited

Arcoverde, Angelina Regina Dos Reis. “Self-regulated learning of Natural Sciences and Mathematics future teachers: Learning strategies, self-efficacy, and socio-demographic factors.” Self-regulated learning of Natural Sciences and Mathematics future teachers: Learning strategies, self-efficacy, and socio-demographic factors, 2002, https://pmc.ncbi.nlm.nih.gov/articles/PMC8727736/#:~:text=The%20self%2Dregulated%20learning%20model%20proposed%20by%20Zimmerman%20(2000%2C,success%20in%20future%20learning%20situations. Accessed 7 April 2026.

Dunlosky, John. “Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology.” PubMed, 2013, https://pubmed.ncbi.nlm.nih.gov/26173288/. Accessed 7 April 2026.

Skarlatidou, Artemis. “Design approaches and human–computer interaction methods to support user involvement in citizen science.” Design approaches and human–computer interaction methods to support user involvement in citizen science, 2021, https://www.jstor.org/content/oa_chapter_edited/j.ctv15d8174.11?seq=10. Accessed 7 April 2026.

Timeline:

January 1-15, 2027

Design survey

January 16, 2027 - January 25, 2027

Finish survey and find participants

January 26, 2027, - February 5, 2027

Collect data

February 6, 2027 - February 25, 2027

Analyze data

February 26, 2027 - April 5, 2027

Draft report

April 6, 2027 - April 15, 2027

Finish report

Item Unit and cost Total

Participant incentive—–$10/person 20 people—–$200

Survey platform—–Google Forms(entirely free)—–$0

Data storage—–UCF onedrive(entirely free)—–$0

Printing costs—–final report materials—–$25

Misc research expenses—–Back up usb—–$30

Total $255