Taking notes on laptops rather than in longhand is increasingly common. Many researchers have suggested that laptop note taking is less effective than longhand note taking for learning. Prior studies have primarily focused on students' capacity for multitasking and distraction when using laptops. The present research suggests that even when laptops are used solely to take notes, they may still be impairing learning because their use results in shallower processing. In three studies, we found that students who took notes on laptops performed worse on conceptual questions than students who took notes longhand. We show that whereas taking more notes can be beneficial, laptop note takers' tendency to transcribe lectures verbatim rather than processing information and reframing it in their own words is detrimental to learning.

/pdf/the-pen-is-mightier-than-the-keyboard-advantages-of-longhand-1o1unc4ahk.pdf

The Pen Is Mightier Than the Keyboard Advantages of Longhand Over Laptop Note Taking

https://interruptions.net/literature/Bailey-CHB06_1.pdf

On the need for attention-aware systems : Measuring effects of interruption on task performance, error rate, and affective state

We performed an empirical study to investigate whether the context of interruptions makes a difference. We found that context does not make a difference but surprisingly, people completed interrupted tasks in less time with no difference in quality. Our data suggests that people compensate for interruptions by working faster, but this comes at a price: experiencing more stress, higher frustration, time pressure and effort. Individual differences exist in the management of interruptions: personality measures of openness to experience and need for personal structure predict disruption costs of interruptions. We discuss implications for how system design can support interrupted work.

/pdf/the-cost-of-interrupted-work-more-speed-and-stress-1wpkt77qok.pdf

The cost of interrupted work: more speed and stress

Application of physiological methods, in particular electroencephalography (EEG), offers new and promising approaches to educational psychology research. EEG is identified as a physiological index that can serve as an online, continuous measure of cognitive load detecting subtle fluctuations in instantaneous load, which can help explain effects of instructional interventions when measures of overall cognitive load fail to reflect such differences in cognitive processing. This paper presents a review of seminal literature on the use of continuous EEG to measure cognitive load and describes two case studies on learning from hypertext and multimedia that employed EEG methodology to collect and analyze cognitive load data.

Using Electroencephalography to Measure Cognitive Load

The World Wide Web provides an abundant source of medical information. This information can assist people who are not healthcare professionals to better understand health and illness, and to provide them with feasible explanations for symptoms. However, the Web has the potential to increase the anxieties of people who have little or no medical training, especially when Web search is employed as a diagnostic procedure. We use the term cyberchondria to refer to the unfounded escalation of concerns about common symptomatology, based on the review of search results and literature on the Web. We performed a large-scale, longitudinal, log-based study of how people search for medical information online, supported by a survey of 515 individuals' health-related search experiences. We focused on the extent to which common, likely innocuous symptoms can escalate into the review of content on serious, rare conditions that are linked to the common symptoms. Our results show that Web search engines have the potential to escalate medical concerns. We show that escalation is associated with the amount and distribution of medical content viewed by users, the presence of escalatory terminology in pages visited, and a user's predisposition to escalate versus to seek more reasonable explanations for ailments. We also demonstrate the persistence of postsession anxiety following escalations and the effect that such anxieties can have on interrupting user's activities across multiple sessions. Our findings underscore the potential costs and challenges of cyberchondria and suggest actionable design implications that hold opportunity for improving the search and navigation experience for people turning to the Web to interpret common symptoms.

/pdf/cyberchondria-studies-of-the-escalation-of-medical-concerns-578w75nz0w.pdf

Cyberchondria: Studies of the escalation of medical concerns in Web search

Advances in artificial intelligence (AI) frame opportunities and challenges for user interface design. Principles for human-AI interaction have been discussed in the human-computer interaction community for over two decades, but more study and innovation are needed in light of advances in AI and the growing uses of AI technologies in human-facing applications. We propose 18 generally applicable design guidelines for human-AI interaction. These guidelines are validated through multiple rounds of evaluation including a user study with 49 design practitioners who tested the guidelines against 20 popular AI-infused products. The results verify the relevance of the guidelines over a spectrum of interaction scenarios and reveal gaps in our knowledge, highlighting opportunities for further research. Based on the evaluations, we believe the set of design guidelines can serve as a resource to practitioners working on the design of applications and features that harness AI technologies, and to researchers interested in the further development of human-AI interaction design principles.

Guidelines for Human-AI Interaction

Notifications can have reduced interruption cost if delivered at moments of lower mental workload during task execution. Cognitive theorists have speculated that these moments occur at subtask boundaries. In this article, we empirically test this speculation by examining how workload changes during execution of goal-directed tasks, focusing on regions between adjacent chunks within the tasks, that is, the subtask boundaries. In a controlled experiment, users performed several interactive tasks while their pupil dilation, a reliable measure of workload, was continuously measured using an eye tracking system. The workload data was extracted from the pupil data, precisely aligned to the corresponding task models, and analyzed. Our principal findings include (i) workload changes throughout the execution of goal-directed tasks; (ii) workload exhibits transient decreases at subtask boundaries relative to the preceding subtasks; (iii) the amount of decrease tends to be greater at boundaries corresponding to the completion of larger chunks of the task; and (iv) different types of subtasks induce different amounts of workload. We situate these findings within resource theories of attention and discuss important implications for interruption management systems.

Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management

We report on a field study of the multitasking behavior of computer users focused on the suspension and resumption of tasks. Data was collected with a tool that logged users' interactions with software applications and their associated windows, as well as incoming instant messaging and email alerts. We describe methods, summarize results, and discuss design guidelines suggested by the findings.

/pdf/disruption-and-recovery-of-computing-tasks-field-study-1mm9aafuvt.pdf

Disruption and recovery of computing tasks: field study, analysis, and directions

Accurate assessment of a user's mental workload will be critical for developing systems that manage user attention (interruptions) in the user interface. Empirical evidence suggests that an interruption is much less disruptive when it occurs during a period of lower mental workload. To provide a measure of mental workload for interactive tasks, we investigated the use of task-evoked pupillary response. Results show that a more difficult task demands longer processing time, induces higher subjective ratings of mental workload, and reliably evokes greater pupillary response at salient subtasks. We discuss the findings and their implications for the design of an attention manager.

https://interruptions.net/literature/Iqbal-CHI04-p1477-iqbal.pdf

Task-evoked pupillary response to mental workload in human-computer interaction

To contribute to systems that reason about human attention, our work empirically demonstrates how a user's mental workload changes during task execution. We conducted a study where users performed interactive, hierarchical tasks while mental workload was measured through the use of pupil size. Results show that (i) different types of subtasks impose different mental workload, (ii) workload decreases at subtask boundaries, (iii) workload decreases more at boundaries higher in a task model and less at boundaries lower in the model, (iv) workload changes among subtask boundaries within the same level of a task model, and (v) effective understanding of why changes in workload occur requires that the measure be tightly coupled to a validated task model. From the results, we show how to map mental workload onto a computational Index of Opportunity that systems can use to better reason about human attention.

Shamsi T. Iqbal

Papers

Guidelines for Human-AI Interaction

Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management

Disruption and recovery of computing tasks: field study, analysis, and directions

Task-evoked pupillary response to mental workload in human-computer interaction

Towards an index of opportunity: understanding changes in mental workload during task execution