- Consider task feasibility
- Focus on actionable task 
- Leverage context 
- Realize potential for personalization 

Further Implications of Microtask Detection Study

The results show the strong performance of machine-learned model for automatically detecting microtasks. Analysis show an importance of the behavioral features and the strength of the model based on textual features. Some desire in integrating features related to the user’s task history. 

University of Michigan - Ann Arbor

Study looks at how to break up, estimate and plan micro tasking using information systems and computer programs to suggest timing throughout the day to complete certain timed tasks, it would use a task management application. Proposing the use of information systems as a way to plan and prose certain microtasks throughout the day. 

Microtask Detection Study

White, Ryen W., et al. “Microtask Detection.” ACM Transactions on Information Systems, vol. 39, no. 2, 2021, pp. 1–29., https://doi.org/10.1145/3432290. 

Microtask Detection Reference 

A system designed to collect, store, and distribute information. Can be broken down by four components: task, people, structure, and technology. 

Information System

Diagram of the path one can go through to complete a task, including the steps and decisions throughout the process.

Task Model

The process to which one manages their set number of tasks.

Task Management 

- Microtask detection, including household chores
- Required actions
- Making arrangements
- Performing work tasks
- Relaxation

Which Microtasks the Application Detects 

- Introduce microtask detection as an important new challenge in task and time management research and in general research on information access and use.
- Build machine-learned models to detect microtasks and demonstrate their performance and abilities 
- Develop an actionable task preclassifier and show that preclassification is necessary to realize reasonable classification performance from microtask detection on general task lists 
- Show the implications of microtask detection for the design of information systems to help people more fully utilize their time


Reasoning for Microtask Detection Study

Because tasks are so important to our daily lives, they have been studied extensively. Previous studies have shown findings surrounding these concepts: 
- Time estimation : Previous studies have shown that estimating time has revealed many cognitive biases that impact how one accurately estimated the duration of a task. 
- Task assistance: Though there are many applications nowadays, they still requires that the systems have a certain knowledge of which tasks are microtasks. 
- Productivity: This study differs from microproductivity research. Instead of focusing on larger productivity, like a large project, this study focuses on short, non-committal tasks that can be carried out throughout a normal day. These tasks do not need to be part of a larger task, and are usually user-generated.  
- Crowdsourcing: Crowdworkers are provided with short, context-free tasks that they are able to complete at any time, but usually with a certain duration goal. While this is important, this study focuses on the average person’s daily tasks, not the goals of crowdworkers. 
- Tasks in search and retrieval: People use search systems to finish a range of tasks and this duration has been used to program search algorithms. Evidence of this task completion is observed in previous search log data. Search can play a role in the completion of microtasks after they’ve been identified, helping find the required resources (digital or physical). 

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