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Detecting the Disengaged Reader - Using Scrolling Data to Predict Disengagement during Reading
This article examines the necessary elements of an adaptive system - the knowledge domain model, the user model, adaptivity mechanism, and explanation model - and the impact of each on the potential effectiveness of existing and potentially possible systems. Special attention is paid to the individual characteristics that creators of adaptive systems use to build a user model. These characteristics can be grouped into 4 categories corresponding to cognitive, affective, behavioral/psychomotor, and mixed domains. The article analyzes methods for determining user characteristics and possible ways to identify them more accurately. The article also proposes currently unused adaptivity mechanisms that focus more on mastering new tools and instruments rather than knowledge per se. In particular, it explores human-computer interaction in both individual and group formats, involving both students and teachers. In conclusion, the prospects of using artificial intelligence and collaborative tools in creating and improving adaptive systems are described, emphasizing the need for interdisciplinary collaboration and consideration of complex cognitive process models while creating and testing the systems.
Digital distractions can interfere with goal attainment and lead to undesirable habits that are hard to get red rid of. Various digital self‐control interventions promise support to alleviate the negative impact of digital distractions. These interventions use different approaches, such as the blocking of apps and websites, goal setting, or visualizations of device usage statistics. While many apps and browser extensions make use of these features, little is known about their effectiveness. This systematic review synthesizes the current research to provide insights into the effectiveness of the different kinds of interventions. From a search of the ‘ACM’, ‘Springer Link’, ‘Web of Science’, ’IEEE Xplore’ and ‘Pubmed’ databases, we identified 28 digital self‐control interventions. We categorized these interventions according to their features and their outcomes. The interventions showed varying degrees of effectiveness, and especially interventions that relied purely on increasing the participants' awareness were barely effective. For those interventions that sanctioned the use of distractions, the current literature indicates that the sanctions have to be sufficiently difficult to overcome, as they will otherwise be quickly dismissed. The overall confidence in the results is low, with small sample sizes, short study duration, and unclear study contexts. From these insights, we highlight research gaps and close with suggestions for future research.
Time Series Classification (TSC) involves building predictive models for a discrete target variable from ordered, real valued, attributes. Over recent years, a new set of TSC algorithms have been developed which have made significant improvement over the previous state of the art. The main focus has been on univariate TSC, i.e. the problem where each case has a single series and a class label. In reality, it is more common to encounter multivariate TSC (MTSC) problems where the time series for a single case has multiple dimensions. Despite this, much less consideration has been given to MTSC than the univariate case. The UCR archive has provided a valuable resource for univariate TSC, and the lack of a standard set of test problems may explain why there has been less focus on MTSC. The UEA archive of 30 MTSC problems released in 2018 has made comparison of algorithms easier. We review recently proposed bespoke MTSC algorithms based on deep learning, shapelets and bag of words approaches. If an algorithm cannot naturally handle multivariate data, the simplest approach to adapt a univariate classifier to MTSC is to ensemble it over the multivariate dimensions. We compare the bespoke algorithms to these dimension independent approaches on the 26 of the 30 MTSC archive problems where the data are all of equal length. We demonstrate that four classifiers are significantly more accurate than the benchmark dynamic time warping algorithm and that one of these recently proposed classifiers, ROCKET, achieves significant improvement on the archive datasets in at least an order of magnitude less time than the other three.
Advertisers have to pay publishers for "viewable" ads, irrespective of whether the users paid active attention. In this paper, we suggest that a granular analysis of users' viewing patterns can help us to progress beyond mere "viewability" and toward actual differentiation of whether a user has paid attention to an ad or not. To this end, we use individual viewport trajectories, which measures the sequence of locations and times an object (e.g., an ad) is visible on the display of a device (desktop or mobile). To validate our model and benchmark it against the extant models, such as the "viewability" policy (50% threshold) model, we use data from an eye-tracking experiment. Findings confirm the improved model fit, highlight distinct viewing patterns in the data, and inform information processing on mobile phones. Consequently, implications are relevant to publishers, advertisers, and consumer researchers.
Based on the analysis of 190 studies (18,573 participants), we estimate that the average silent reading rate for adults in English is 238 words per minute (wpm) for non-fiction and 260 wpm for fiction. The difference can be predicted by taking into account the length of the words, with longer words in non-fiction than in fiction. The estimates are lower than the numbers often cited in scientific and popular writings. The reasons for the overestimates are reviewed. The average oral reading rate (based on 77 studies and 5,965 participants) is 183 wpm. Reading rates are lower for children, old adults, and readers with English as second language. The reading rates are in line with maximum listening speed and do not require the assumption of reading-specific language processing. Within each group/task there are reliable individual differences, which are not yet fully understood. For silent reading of English non-fiction most adults fall in the range of 175 to 300 wpm; for fiction the range is 200 to 320 wpm. Reading rates in other languages can be predicted reasonably well by taking into account the number of words these languages require to convey the same message as in English.
Much has been written about why students engage in academic studies at university, with less attention given to the concept of disengagement. Understanding the risks and factors associated with student disengagement from learning provides opportunities for targeted remediation. The aims of this review were to 1) explore how student disengagement has been conceptualised, 2) identify factors associated with disengagement and 3) identify measureable indicators of disengagement in previous literature. A systematic search was conducted across relevant databases and key websites. Reference lists of included papers were screened for additional publications. Studies and national published survey data were included if they addressed issues pertaining to student disengagement with learning or the academic environment, were in full text and in English. In the 32 papers that met the inclusion criteria, student disengagement was conceptualised as a multi-faceted, complex yet fluid state that has a combination of behavioural, emotional and cognitive domains influenced by intrinsic (psychological factors, low motivation, inadequate preparation for higher education and unmet or unrealistic expectations) or extrinsic (competing demands, institutional structure and processes, teaching quality and online teaching and learning). A number of measurable indicators of disengagement were synthesised from the literature including those that were self-reported by students and those collected by an institution. An examination of the conceptualisation, influences and indicators of disengagement could inform intervention programs to ameliorate the consequences of disengagement for students and academic institutions.
In the last 5 years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive. The archive has recently been expanded to 85 data sets, over half of which have been donated by researchers at the University of East Anglia. Aspects of previous evaluations have made comparisons between algorithms difficult. For example, several different programming languages have been used, experiments involved a single train/test split and some used normalised data whilst others did not. The relaunch of the archive provides a timely opportunity to thoroughly evaluate algorithms on a larger number of datasets. We have implemented 18 recently proposed algorithms in a common Java framework and compared them against two standard benchmark classifiers (and each other) by performing 100 resampling experiments on each of the 85 datasets. We use these results to test several hypotheses relating to whether the algorithms are significantly more accurate than the benchmarks and each other. Our results indicate that only nine of these algorithms are significantly more accurate than both benchmarks and that one classifier, the collective of transformation ensembles, is significantly more accurate than all of the others. All of our experiments and results are reproducible: we release all of our code, results and experimental details and we hope these experiments form the basis for more robust testing of new algorithms in the future.
Engagement during reading can be measured by the amount of time readers invest in the reading process. It is hypothesized that disengagement is marked by a decrease in time investment as compared with the demands made on the reader by the text. In this study, self-paced reading times for screens of text were predicted by a text complexity score called formality; formality scores increase with cohesion, informational content/genre, syntactic complexity, and word abstractness as measured by the Coh-Metrix text-analysis program. Cognitive decoupling is defined as the difference between actual reading times and reading times predicted by text formality. Decoupling patterns were found to differ as a function of the serial position of the screens of text and the text genre (i.e., informational, persuasive, and narrative) but surprisingly not as a function of reader characteristics (reading speed and comprehension). This underscores the importance of mining text characteristics in addition to individual differences and task constraints in understanding engagement during reading.
All forms of learning take time. There is a large body of research suggesting that the amount of time spent on learning can improve the quality of learning, as represented by academic performance. The wide-spread adoption of learning technologies such as learning management systems (LMSs), has resulted in large amounts of data about student learning being readily accessible to educational researchers. One common use of this data is to measure time that students have spent on different learning tasks (i.e., time-on-task). Given that LMS systems typically only capture times when students executed various actions, time-on-task measures are estimated based on the recorded trace data. LMS trace data has been extensively used in many studies in the field of learning analytics, yet the problem of time-on-task estimation is rarely described in detail and the consequences that it entails are not fully examined.
This paper presents the results of a study that examined the effects of different time-on-task estimation methods on the results of commonly adopted analytical models. The primary goal of this paper is to raise awareness of the issue of accuracy and appropriateness surrounding time-estimation within the broader learning analytics community, and to initiate a debate about the challenges of this process. Furthermore, the paper provides an overview of time-on-task estimation methods in educational and related research fields.
Web Search has seen two big changes recently: rapid growth in mobile search traffic, and an increasing trend towards providing answer-like results for relatively simple information needs (e.g., [weather today]). Such results display the answer or relevant infor-mation on the search page itself without requiring a user to click. While clicks on organic search results have been used extensively to infer result relevance and search satisfaction, clicks on answer-like results are often rare (or meaningless), making it challenging to evaluate answer quality. Together, these call for better measure-ment and understanding of search satisfaction on mobile devices. In this paper, we studied whether tracking the browser viewport (visible portion of a web page) on mobile phones could enable ac-curate measurement of user attention at scale, and provide good measurement of search satisfaction in the absence of clicks. Fo-cusing on answer-like results in web search, we designed a lab study to systematically vary answer presence and relevance (to the user's information need), obtained satisfaction ratings from users, and simultaneously recorded eye gaze and viewport data as users performed search tasks. Using this ground truth, we identified increased scrolling past answer and increased time below answer as clear, measurable signals of user dissatisfaction with answers. While the viewport may contain three to four results at any given time, we found strong correlations between gaze duration and view-port duration on a per result basis, and that the average user atten-tion is focused on the top half of the phone screen, suggesting that we may be able to scalably and reliably identify which specific result the user is looking at, from viewport data alone.
Learning environments aim to deliver efficacious instruction, but rarely take into consideration the motivational factors involved in the learning process. However, motivational aspects like engagement play an important role in effective learning-engaged learners gain more. E-Learning systems could be improved by tracking students' disengagement that, in turn, would allow personalized interventions at appropriate times in order to reengage students. This idea has been exploited several times for Intelligent Tutoring Systems, but not yet in other types of learning environments that are less structured. To address this gap, our research looks at online learning-content-delivery systems using educational data mining techniques. Previously, several attributes relevant for disengagement prediction were identified by means of log-file analysis on HTML-Tutor, a web-based learning environment. In this paper, we investigate the extendibility of our approach to other systems by studying the relevance of these attributes for predicting disengagement in a different e-learning system. To this end, two validation studies were conducted indicating that the previously identified attributes are pertinent for disengagement prediction, and two new meta-attributes derived from log-data observations improve prediction and may potentially be used for automatic log-file annotation.
This paper is an exposition of an algorithm for text analysis that can be of value to writers and documentalists. The simplicity of this algorithm allows it to be easily programmed on most computer systems. The author has successfully implemented this test as a function within a text editing system written in RPG II. Included in this paper is a sample program written for the VAX 11/780 in PL/I.In 1949 Dr. Rudolph Flesch published a book titled “The Art of Readable Writing.” In this book, he described a manual method of reading ease analysis. This method was to analyze text samples of about 100 words. Each sample is assigned a readability index based upon the average number of syllables per word and the average number of words per sentence. This Flesch Index is designed so that most scores range from 0 to 100. Only college graduates are supposed to follow prose in the 0 - 30 range. Scores of 50 -60 are high-school level and 90 - 100 should be readable by fourth graders.Though crude, since it is designed simply to reward short words and sentences, the index is useful. It gives a basic, objective idea of how hard prose is to wade through. This test has been used by some state insurance commissions to enforce the readability of policies.Flesch's algorithm was automated in the early 1970s by the Service Research Group of the General Motors Corporation. The program, called GM-STAR (General Motors Simple Test Approach for Readability) was used so that shop manuals could be made more readable. GM-STAR was originally written in BASIC language. The key to this program is a very simple algorithm to count the number of syllables in a word. In general the text analysis portion of the program uses the following rules:Periods, explanation points, question marks, colons and semi-colons count as end-of-sentence marks.Each group of continuous non-blank characters counts as a word.Each vowel (a, e, i, o, u, y) in a word counts as one syllable subject to the following sub-rules:Ignore final -ES, -ED, -E (except for -LE)Words of three letters or less count as one syllableConsecutive vowels count as one syllable.Although there are many exceptions to these rules, it works in a remarkable number of cases.The Flesch Index (F) for a given text sample is calculated from three statistics;The total number of sentences (N),The total number of words (W),The total number of syllables (L),according to the following formula: F = 206.835 - 1.015 × (W/N) - 84.6 × (L/W).The Grade Level Equivalent (G) of the Flesch Index is given by the following table:If -50 F 50, then G = (140 - F)/6.66If 50 F 60, then G = (93 - F)/3.33If 60 F 70, then G = (110 - F)/5.0If 70 F , then G = (150 - F)/10.0A PL/I program that implements this algorithm is listed below along with sample output. For simplicity, this program assumes all letters are in upper case. Processing text with lower case letters can be accomplished by either modifying the program to test for lower case as well as upper case, or by preprocessing the text sample to translate all letters to upper case. There are a multitude of other refinements and amenities that can be added to the basic analysis. Among these are:Nothing which characters are considered sentence terminators.Ignoring periods that are used for abbreviations rather than sentence terminators.Ignoring word connecting hyphens in compound words.Noting which character groups should probably be spelled out, such as numerals and dollar amounts.Sharpening the syllable counting routine to detect exceptional cases.
A large proportion of thoughts are internally generated. Of these, mind wandering—when attention shifts away from the current activity to an internal stream of thought—is frequent during reading and is negatively related to comprehension outcomes. Our goal is to review research on mind wandering during reading with an interdisciplinary and integrative lens that spans the cognitive, behavioural, computing and intervention sciences. We begin with theoretical developments on mind wandering, both in general and in the context of reading. Next, we discuss psychological research on how the text, context and reader interact to influence mind wandering and on associations between mind wandering and reading outcomes. We integrate the findings in a (working) theoretical account of mind wandering during reading. We then turn to computational models of mind wandering, including a short tutorial with examples on how to use machine learning to construct these models. Finally, we discuss emerging intervention research aimed at proactively reducing the occurrence of mind wandering or mitigating its effects. We conclude with open questions and directions for future research.
The prevalence of the acronym tl;dr (“too long; didn’t read”) suggests that people intentionally disengage their attention from long sections of text. We studied this real-world phenomenon in an educational context by measuring rates of intentional and unintentional mind-wandering while undergraduate student participants (n = 80) read academic passages that were presented in either short sections of text (one sentence per screen) or relatively long sections (2–6 sentences per screen). We found that participants were significantly more likely to unintentionally disengage their attention while reading the longer sections of text, whereas intentional mind-wandering rates were equivalent across short and long sections of text. The difference in unintentional mind-wandering rates suggests that section length may serve as a cue that people use to assess the cost-benefit tradeoffs involved in attending to (or disengaging from) text. We conclude that instructors should avoid presenting electronic reading material in long sections of text.
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series. Time series data gives rise to various distinct but closely related learning tasks, such as forecasting and time series classification, many of which can be solved by reducing them to related simpler tasks. We discuss the main rationale for creating a unified interface, including reduction, as well as the design of sktime's core API, supported by a clear overview of common time series tasks and reduction approaches.
Website measures of engagement captured from millions of users, such as in-page scrolling and viewport position, can provide deeper understanding of attention than possible with simpler measures, such as dwell time. Using data from 1.2M news reading sessions, we examine and evaluate three increasingly sophisticated models of sub-document attention computed from viewport time, the time a page component is visible on the user display. Our modeling incorporates prior eye-tracking knowledge about onscreen reading, and we validate it by showing how, when used to estimate user reading rate, it aligns with known empirical measures. We then show how our models reveal an interaction between article topic and attention to page elements. Our approach supports refined large-scale measurement of user engagement at a level previously available only from lab-based eye-tracking studies.
Prior work on user engagement with online media identified web page dwell time as a key metric reflecting level of user engagement with online news articles. While on average, dwell time gives a reasonable estimate of user experience with a news article, it is not able to capture important aspects of user interaction with the page, such as how much time a user spends reading the article vs. viewing the comment posted by other users, or the actual proportion of article read by the user. In this paper, we propose a set of user engagement classes along with new user engagement metrics that, unlike dwell time, more accurately reflect user experience with the content. Our user engagement classes provide clear and interpretable taxonomy of user engagement with online news, and are defined based on amount of time user spends on the page, proportion of the article user actually reads and the amount of interaction users performs with the comments. Moreover, we demonstrate that our metrics are relatively easier to predict from the news article content, compared to the dwell time, making optimization of user engagement more attainable goal.
This research predicted behavioral disengagement using quitting behaviors while learning from instructional texts. Supervised machine learning algorithms were used to predict if students would quit an upcoming text by analyzing reading behaviors observed in previous texts. Behavioral disengagement (quitting) at any point during the text was predicted with an accuracy of 76.5% (48% above chance), before students even began engaging with the text. We also predicted if a student would quit reading on the first page of a text or continue reading past the first page with an accuracy of 88.5% (29% above chance), as well as if students would quit sometime after the first page with an accuracy of 81.4% (51% greater than chance). Both actual quits and predicted quits were significantly related to learning, which provides some evidence for the predictive validity of our model. Implications and future work related to ITSs are also discussed.
We investigated the frequency and duration of distractions and media multitasking among college students engaged in a 3-h solitary study/homework session. Participant distractions were assessed with three different kinds of apparatus with increasing levels of potential intrusiveness: remote surveillance cameras, a head-mounted point-of-view video camera, and a mobile eyetracker. No evidence was obtained to indicate that method of assessment impacted multitasking behaviors. On average, students spent 73 min of the session listening to music while studying. In addition, students engaged with an average of 35 distractions of 6 s or longer over the course of 3 h, with an aggregated mean duration of 25 min. Higher homework task motivation and self-efficacy to concentrate on homework were associated with less frequent and shorter duration multitasking behaviors, while greater negative affect was linked to longer duration multitasking behaviors during the session. We discuss the implications of these data for assessment and for understanding the nature of distractions and media multitasking during solitary studying.
- Markus Löning
- Franz Király
- Tony Bagnall
- Matthew Middlehurst
- Sajaysurya Ganesh
- George Oastler
- Jason Lines
- Martin Walter
- Lukasz Viktorkaz
- Mentel
- Chrisholder
- Leonidas Rnkuhns
- Taiwo Tsaprounis
- Patrick Owoseni
- Ciaran Rockenschaub
- Guzal Gilbert
- Bulatova
- Mirae Lovkush
- Kejsi Parker
- Patrick Take
- Stanislav Schäfer
- Khrapov
- Marie Svea
- Meyer
- Yi-Xuan Aidenrushbrooke
- Xu
Markus Löning, Franz Király, Tony Bagnall, Matthew Middlehurst, Sajaysurya
Ganesh, George Oastler, Jason Lines, Martin Walter, ViktorKaz, Lukasz Mentel,
chrisholder, RNKuhns, Leonidas Tsaprounis, Taiwo Owoseni, Patrick Rockenschaub, danbartl, jesellier, eenticott shell, Ciaran Gilbert, Guzal Bulatova, Lovkush,
Mirae Parker, Kejsi Take, Patrick Schäfer, Stanislav Khrapov, Svea Marie Meyer,
AidenRushbrooke, oleskiewicz, Yi-Xuan Xu, and Afzal Ansari. 2022. alan-turinginstitute/sktime: v0.13.2. https://doi.org/10.5281/zenodo.7017832