Master thesis spring Anders Gimmestad Gule Calendars as User Context Providers in an E-learning Environment Supervisor: Rune Hjelsvold Apropos-Internett: Dag Olaf Berg

outline introduction problem methodology part one part two conclusion future work

Web courses are often neglected by the students/participants, other tasks are prioritized. Context-aware systems tries to read a context (situation), and act accordingly. With a known context (past, present or future) a context-aware LMS could assist the student in the process of planning/suggesting course modules. Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work Context in an E-learning environment

Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work «Context is any information that can be used to characterize the situation of an entity. An entity is a person, place or object that is considered relevant to the interaction between a user and a application, including the user and applications themselves.» Anind K. Dey, «Understanding and using context» Context Potentially, a calendar can describe an entity’s (who) location (where) and it’s resources (what) in a time set (when). A calendar potentially form a reliable and stable source for context data. Calendar as a sensor

Related work - centered around the algorithms. - little or no literature on the reliability of calendars. Context is inconstant - it always changes. - hard to capture correctly. Calendars are made by people - people are lazy. - content and details are user dependent. - can calendars be considered reliable? Adapting to user context - context features are not determined or not assessed. - how do the features affect the results when utilized? What is the problem? Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work

Q1 How do the correlation between different user types and users’ motivation affect the contents in their calendars? Q2 How suited are real-world calendars as candidates for user context extraction? Q3 What user context features should be considered in a context-aware planning algorithm, and how do they affect the result? Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work What are the unknowns?

Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work Calendar analysis - gather calendars - compare - analyze Feature identification - interviews - analysis Prototype testing/analysis - use cases - scenarios Literature research Study of users - interviews - internet-survey Part onePart two Methodology

Study of calendar users Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work Students Motivated by: - personal gains - organizing, remember Professionals Motivated by: - job environment - organizing and managing Survey 33 participants, 10 students - 23 professionals

Study of calendar users (cont.) Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work Motivation and content Shared calendars

Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work Professionals have strong reasons for motivation (, and a shared calendar strengthens this motivation further). - consequence; a professional’s calendar is a «better» than a student’s - we need to separate students and professionals in further analysis. Significant correlation between motivation and calendar details/accuracy. - a motivated user generates a «good» calendar. - users who understand the benefits tend to provide a more detailed calendar What do we do now? Study of calendar users (cont.)

Points of interest - categorization of entries - entry content - entry clusters - users Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work Study of calendars Analysis 13 people x 2 weeks of calendar data - 8 professionals, 5 students - 26 weeks, 442 entries how much information can be extracted about the user?

Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work Study of calendars (cont.) Entry categorization 38.9 % (172 of 442) unidentifiable entries % (83 of 121) unidentifiable student entries % (89 of 321) unidentifiable prof. entries Entry content Location - 41 % (172 of 442) with location attribute - place names and room number most commonly used as descriptor. - not enough Descriptor - «meeting with John» - high quantity, but low quality

Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work Study of calendars (cont.) Content Problem: low quality of entry descriptors. Suggested remedy: User Model Users Users who:... produce small amount of entries and minimal level of detailed content. (50 %, mostly students).... only include the most important details (35 %).... act highly organized and produce a high amount of detailed content (15 %). Confirms our initial results form the survey. What do we do now? Conclusion of part one: Calendars have a potential

Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work context features - day - time of day - ideal/bad times - reserved times - time between entries - observations Analysis 10 volunteers, interviewed about their planning preferences. - 5 students and 5 professionals. - questions/conversation - practical task User planning behavior

Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work User planning behavior (cont.) Context features - all features are significant to the participants. - students and professionals again have different results. Planning approaches - professionals prioritize the planning according to importance, students according to personal preferences (sleep, food etc.). - professionals reorganize and tweak their calendar, students do not. - plans according to the situation (before/after lunch etc.)

Utilizing context features Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work proof of concept - planning assistant prototype «Plan a course module according to the identified context features.» Prototype - categorization procedure - assess the open slots in the calendar - which day - time at day - time before and after the proposed slot - entries (context) before and after the proposed slot - suggests alternatives to the user

Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work Preliminary conclusions Part one - calendars from motivated users with the presence of a user model, can be considered as reliable sources for user context extraction (a professional’s calendar is best suited for this purpose). Part two - day, time at day, time between entries, personal and other users’ activities are significant context features for the interviewees when planning. - professionals and students differ, other user groups may also. - Uncategorized entries have a large influence to the accuracy of the prototype’s accuracy.

Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work Future work - A categorization/classification algorithm, that can obtain user input and feedback. - Further analysis on the context features’ importance. Which is more important than the other?

Introduction - Problem - Methodology - Part one - Part two - Conclusion - Future work ?