Attention span is the amount of time that learners can spend concentrating on tasks. Sustained attention develops through childhood and into adulthood, with a period of accelerated development occurring during early and middle childhood (Slattery, O’Callaghan, Ryan, Fortune, & McAvinue, 2022).

Attention training is a part of education. Learners are trained to remain focused on discussion topics for extended periods of time and to develop listening and analytical skills in the process. For over a century, there has been keen interest in improving children’s attention in educational contexts.

Adaptive instructional systems, a comparably recent development, adapt instruction based upon learners’ states of engagement, arousal, motivation, prior knowledge, anxiety, and engaged concentration (Sottilare & Goodwin, 2017).

A tutoring strategy for learners in states of engaged concentration might be to “do nothing” because they would already be in ideal states for learning. However, a longer-term tutoring strategy might be to strengthen learners’ capabilities to maintain their states of sustained attention and concentration.

How can adaptive instructional systems scheduling learners’ homework items contribute to increasing their attention spans and concentration?

Computer-administered and strategically, adaptively scheduled educational exercises and activities could be organized into gamified sprints, each sprint containing one or more stages. Learners would be encouraged to only take a break from or to conclude their schoolwork at the completion of a sprint stage and not in the middle of one.

To encourage the sustained, uninterrupted completion of schoolwork, the gamification could be such that a learner would have to repeat an entire sprint stage from the beginning – though not necessarily with the exact same items – if they didn’t finish it before taking a break or concluding. That is, checkpoints or save points could be provided only after sprint stages.

Adaptive instructional systems would control when sprints were presented to learners, the number of stages and items that would be in each, and their other properties including whether or not they would have countdown timers for successful completions. Informed by models of learners, adaptive instructional systems would be able to create and to utilize sprints to encourage capable learners to complete just one more item or just a few more items.

The goal posts for individual learners’ daily and weekly educational exercises and activities would be placed by adaptive instructional systems to be, on average, just a bit ahead of their comfort zones but within their performance capabilities.

What differentiates homework items from one another in terms of their attentional, concentrative, and other cognitive demands? For each item, for each learner, for a pace of progression, which cognitive reservoirs are depleted and to which extents? At which rates do individual learners’ various cognitive reservoirs replenish? Which cognitive reservoirs exist alongside redundant others, and which do not?

Cognitive load can be defined as a multidimensional construct representing the load that performing a particular task imposes on a learner’s cognitive system. The construct has a causal dimension reflecting the interaction between task and learner characteristics and an assessment dimension reflecting the measurable concepts of mental load, mental effort, and performance. Task characteristics that have been identified in previous research include task format, task complexity, uses of multimedia, time pressure, and the pacing of instruction (Paas, Tuovinen, Tabbers, & Van Gerven, 2003).

Xie and Salvendy (2000) distinguished between instantaneous load, peak load, accumulated load, average load, and overall load. Instantaneous load represents the dynamics of cognitive load, which fluctuate each moment that a learner works on a task. Peak load is the maximum value of instantaneous load while working on a task. Accumulated load is the total amount of load that a learner experiences during a task. Average load represents the mean intensity of load during the performance of a task. Overall load is the experienced load based on the whole working procedure or the mapping of instantaneous load or accumulated and average load in the learner’s brain.

Cognitive fatigue can be understood to be an “executive failure to maintain and optimize performance over acute but sustained cognitive effort resulting in performance that is lower and more variable than the individual’s optimal ability” (Holtzer, Shuman, Mahoney, Lipton, & Verghese, 2010). Cognitive fatigue typically develops gradually over time as a person engages in prolonged and demanding mental activities.

Cognitive fatigue may be assessed either subjectively or objectively. Subjective cognitive fatigue involves learners’ perceptions of their exhaustion. Objective cognitive fatigue is measured by changes in cognitive performance relative to a baseline (Karim, Pavel, Nikanfar, Hebri, Roy, Nambiappan, Jaiswal, Wylie, & Makedon, 2024).

While learners can express subjective cognitive fatigue to adaptive instructional systems at any point, considered, here, are automatically detecting learners’ instantaneous, accumulated, and overall cognitive load and objective cognitive fatigue as they progress through and complete strategically, adaptively scheduled homework items from one or more courses.

For over a century, there has been keen interest in improving children’s attention in educational contexts. There have been, thus far, three broad approaches to strengthening attention: attention network training, attention state training, and attention strategy training.

The first approach, attention network training – also referred to as cognitive training or brain training – involves the repetitive practice of cognitive tasks specifically thought to exercise neural networks related to attention.

Adaptive instructional systems could, in theory, intersperse attention-related cognitive tasks into learners’ multi-course, multi-objective schedules of homework items. However, a review of 14 attention network training intervention studies from 1999 to 2021 found that these cognitive tasks, these approaches, did not reliably improve sustained attention capacity (Slattery, O’Callaghan, Ryan, Fortune, & McAvinue, 2022).

The second approach, attention state training, involves practice designed to train brain states thought to influence attention and other networks. Attention state training may also involve networks but, importantly, does not include cognitive tasks specifically designed to train attentional networks.

Adaptive instructional systems could strategically schedule homework items and incorporate gamification, e.g., sprints, to contribute to the strengthening of attention span and concentration. Such techniques can be used in combination with other attention state training activities such as physical activity and meditation. Adaptive instructional systems could also intersperse meditative and mindfulness activities during learners’ homework activities.

The third approach, strategy training, focuses on practicing strategies that momentarily boost attention.

With respect to adaptive instructional systems and gamification, previous works include explorations into uses of: avatars, badges, progress bars, levels, narratives / stories, special effects, non-player characters, tasks / quests, timers, leaderboards, bonuses / rewards / trophies / collectibles, points, roles, virtual currencies, and maps (Ramadhan, Warnars, & Razak, 2023; Seaborn & Fels, 2015).

Adaptive instructional systems can incorporate gamification to strategically, adaptively schedule learners’ homework items from one or more courses, e.g., into sprints, to motivate learners to sustain attention and concentration.

Over the course of time, these processes should increase learners’ capabilities to their maximum potentials. As a result, learners’ performances in other areas for which sustained attention and concentration are prerequisites should also tend to improve.

Holtzer, Roee, Melissa Shuman, Jeannette R. Mahoney, Richard Lipton, and Joe Verghese. "Cognitive fatigue defined in the context of attention networks." Aging, Neuropsychology, and Cognition 18, no. 1 (2010): 108-128.

Karim, Enamul, Hamza R. Pavel, Sama Nikanfar, Aref Hebri, Ayon Roy, Harish R. Nambiappan, Ashish Jaiswal, Glenn R. Wylie, and Fillia Makedon. "Examining the landscape of cognitive fatigue detection: A comprehensive survey." Technologies 12, no. 3 (2024): 38.

Paas, Fred, Juhani E. Tuovinen, Huib Tabbers, and Pascal W. M. Van Gerven. "Cognitive load measurement as a means to advance cognitive load theory." Educational Psychologist 38, no. 1 (2003): 63-71.

Ramadhan, Arief, Harco L. H. S. Warnars, and Fariza H. A. Razak. "Combining intelligent tutoring systems and gamification: A systematic literature review." Education and Information Technologies (2023): 1-37.

Seaborn, Katie, and Deborah I. Fels. "Gamification in theory and action: A survey." International Journal of Human-computer Studies 74 (2015): 14-31.

Slattery, Éadaoin J., Eoin O’Callaghan, Patrick Ryan, Donal G. Fortune, and Laura P. McAvinue. "Popular interventions to enhance sustained attention in children and adolescents: A critical systematic review." Neuroscience and Biobehavioral Reviews 137 (2022): 104633.

Sottilare, Robert A., and Gregory A. Goodwin. "Adaptive instructional methods to accelerate learning and enhance learning capacity." In International Defense and Homeland Security Simulation Workshop of the I3M Conference. 2017.

Xie, Bin, and Gavriel Salvendy. "Prediction of mental workload in single and multiple tasks environments." International Journal of Cognitive Ergonomics 4, no. 3 (2000): 213-242.