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• On-line courses have made educational material widely accessible to students of varying abilities, backgrounds and styles. There is thus a growing need to accommodate for individual differences in e-learning systems. • We present a new algorithm called EduRank for personalizing educational content to students in e-learning systems that combines collaborative filtering with social choice theory. • EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions. • The algorithm was tested on two data sets containing thousands of students and a million records and was able to outperform the state-of-the-art ranking approaches as well as a domain expert. • EduRank was embedded in a real classroom. It was shown to lead students to solve more difficult questions than an ordering by a domain expert, without reducing their performance. The prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities and backgrounds. There is thus a growing need to accommodate for individual differences in e-learning systems. This paper presents an algorithm called EduRank for personalizing educational content to students that combines a collaborative filtering algorithm with voting methods. EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions. These aspects include grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for each student, rather than ordering them according to the student's predicted score. The EduRank algorithm was tested on two data sets containing thousands of students and a million records. It was able to outperform the state-of-the-art ranking approaches as well as a domain expert. EduRank was used by students in a classroom activity, where a prior model was incorporated to predict the difficulty rankings of students with no prior history in the system. It was shown to lead students to solve more difficult questions than an ordering by a domain expert, without reducing their performance. [ABSTRACT FROM AUTHOR]

Background: This study addresses the suicide risk predicting challenge by exploring the predictive ability of machine learning (ML) models integrated with theory‐driven psychological risk factors in real‐time crisis hotline chats. More importantly, we aimed to understand the specific theory‐driven factors contributing to the ML prediction of suicide risk. Method: The dataset consisted of 17,654 crisis hotline chat sessions classified dichotomously as suicidal or not. We created a suicide risk factors‐based lexicon (SRF), which encompasses language representations of key risk factors derived from the main suicide theories. The ML model (Suicide Risk‐Bert; SR‐BERT) was trained using natural language processing techniques incorporating the SRF lexicon. Results: The results showed that SR‐BERT outperformed the other models. Logistic regression analysis identified several theory‐driven risk factors significantly associated with suicide risk, the prominent ones were hopelessness, history of suicide, self‐harm, and thwarted belongingness. Limitations: The lexicon is limited in its ability to fully encompass all theoretical concepts related to suicide risk, nor to all the language expressions of each concept. The classification of chats was determined by trained but non‐professionals in metal health. Conclusion: This study highlights the potential of how ML models combined with theory‐driven knowledge can improve suicide risk prediction. Our study underscores the importance of hopelessness and thwarted belongingness in suicide risk and thus their role in suicide prevention and intervention. [ABSTRACT FROM AUTHOR]

A COVID-19 pandemic-related rise in suicide rates has been predicted due to social isolation, fear, uncertainty, economic turndown and grief. Detecting an increase in suicide rates is difficult in the absence of real-time data. Alternative data sources for such trends in psychopathology and suicidal behavior must be sought. Data from a national chat-based crisis hotline for the first half of 2019 (pre-COVID-19), were compared to data from the first half of 2020 (during COVID-19). Chat sessions were classified by content and demographics and the data compared between the two time periods. Total chats (n = 6756) were 48% higher during COVID-19 (p <.05). Suicide-related chat (SRC) number was also higher, although the proportion relative to all chats was slightly lower during COVID-19, compared to pre-COVID-19 (p <.05). SRCs increased during the COVID-19 lockdown. The number of severe SRCs resulting in urgent police intervention, increased during the lockdown (April–May 2020) compared with the same period in 2019 (p =.04). Issues of anxiety were higher in 2020 (19.4%) vs. 2019 (16.5%) (p <.00001) while issues of depression were lower (22.4% vs 33%, respectively) (p <.00001). The overall use of chats among adults aged >50 yrs increased during COVID-19 and likewise, the rate of SRCs in this age-group increased 30-fold in this period when compared to pre-COVID-19 (p <.00001). SRCs included more women than men (p <.0001) in both pre-COVID-19 and during the COVID-19 period, when the proportion of women increased from 62% in 2019 to 73% during COVID-19 (p <.0001). The rise in total chats, SRCs and SRCs resulting in police action, commenced during lockdown and was ameliorated by end of the lockdown, indicating that distress created by the lockdown was more impactful than mourning deaths of loved ones, fear and uncertainty, because all these factors persisted beyond the end of the lockdown. Older populations were probably more distressed due to greater risk and less adaptability to isolation, social media and staying home. More calls by women may reflect women's better help-seeking capacity. The increase in SRCs indicates the potential for more suicides and the need for bolstering mental health services and reach-out to older people during pandemic lock-downs. [ABSTRACT FROM AUTHOR]

Personalizing the use of educational mathematics applets to fit learners' characteristics poses a great challenge. The present study adopted a unique approach by comparing personalization processes implemented by a machine to those implemented by a human teacher. Given the different affordances—the machine's access to historical log file data, computation and automatization, and the teacher's mathematical knowledge, pedagogical approach and personal acquaintance—the study hypothesized that different considerations would lead to different personalization and learning outcomes. Mathematical applets were assigned to 77 students in the 4th and 5th grades either by an expert teacher or by an algorithm. The assignment took place in a controlled setting in which the teacher was unaware which students were eventually assigned according to her recommendations. The teacher and the machine each recommended an ordered sequence of ten applets per student. The findings suggest that the teacher-assigned group outperformed the machine-assigned group among 5th graders when the applets were sequenced in increasing order of difficulty. In the 4th grade, only the machine recommended a sequence of increasing difficulty and both groups achieved equal performance. The study concludes that in the case of data-driven personalization processes, machines and teachers should learn from each other's affordances and considerations. [ABSTRACT FROM AUTHOR]