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Altruistic behavior has been defined in economic terms as “…costly acts that confer economic benefits on other individuals” (Fehr & Fischbacher, 2003). In a prisoner's dilemma game, cooperation benefits the group but is costly to the individual (relative to defection), yet a significant number of players choose to cooperate. We propose that people do value rewards to others, albeit at a discounted rate (social discounting), in a manner similar to discounting of delayed rewards (delay discounting). Two experiments opposed the personal benefit from defection to the socially discounted benefit to others from cooperation. The benefit to others was determined from a social discount function relating the individual's subjective value of a reward to another person and the social distance between that individual and the other person. In Experiment 1, the cost of cooperating was held constant while its social benefit was varied in terms of the number of other players, each gaining a fixed, hypothetical amount of money. In Experiment 2, the cost of cooperating was again held constant while the social benefit of cooperating was varied by the hypothetical amount of money earned by a single other player. In both experiments, significantly more participants cooperated when the social benefit was higher.

Taipei Main Station of Taipei Mass Rapid Transit is the busiest transport hub in Taiwan in terms of ridership. Its complex layout and high number of passengers frequently lead to congested transfer traffic patterns. This study examined passengers’ walking trajectories and behaviours and the relationship between crowding and train movement at the transfer concourse on floor B2 of Taipei Main Station to understand the factors of interference and congestion during traffic flow. An improvement plan was subsequently proposed. This study observed that because more passengers situated themselves in the middle cars than the front and rear cars, most boarding and alighting passengers used specific escalators to enter and exit the platform level. In addition, passengers’ walking flow tended to be affected by their personal moving distances, the movement of other passengers and traffic volume. Transfer passengers preferred to use escalators or stairs closer to them, resulting in poor traffic diversion inside the platform. In particular, congestion frequently occurred at the fork near the T junction, where most passenger interferences were recorded. Passengers tended to lean against walls or walk between pillars to mitigate the conflicting flow of movement among them. Other walking trajectory factors included the locations and directions of escalators, stairs and turnstiles. This study used Unity3D software to construct three traffic diversion proposals based on observation records. The proposals were used to simulate and verify improved traffic patterns and mitigate interference. The simulations revealed that moderate changes in the upward and downward directions of escalators could facilitate smoother transfer traffic patterns. Escalators with traversing directions that better adhere to passengers’ traffic patterns may substantially increase passengers’ walking speeds regardless of the direction they are coming from, thereby effectively mitigating congestion at the T junction.

Single behavioural differences between egalitarian and despotic animal societies are often assumed to reflect specific adaptations. However, in the present paper, I will show in an individual–orientated model, how many behavioural traits of egalitarian and despotic virtual societies arise as emergent characteristics. The artificial entities live in a homogeneous world and only aggregate, and upon meeting one another and may perform dominance interactions in which the effects of winning and losing are self–reinforcing. The behaviour of these entities is studied in a similar way to that of real animals. It will be shown that by varying the intensity of aggression only, one may switch from egalitarian to despotic virtual societies. Differences between the two types of society appear to correspond closely to those between despotic and egalitarian macaque species in the real world. In addition, artificial despotic societies show a clearer spatial centrality of dominants and, counter–intuitively, more rank overlap between the sexes than the egalitarian ones. Because of the correspondence with patterns in real animals, the model makes it worthwhile comparing despotic and egalitarian species for socio–spatial structure and rank overlap too. Furthermore, it presents us with parsimonious hypotheses which can be tested in real animals for patterns of aggression, spatial structure and the distribution of social positive and sexual behaviour.

Previous studies have shown that encouraging students to use self-explanation strategies has proven effective in text-focused learning contexts. However, no study to date has focused on how students' strategy preference moderates the effect of self-explanation strategies on learning from video lectures. The current study investigated how students' self-explanation strategy preference impacts their learning from video lectures by using prompts with a between-within-subjects design strategy preference (i.e., strategy preference vs. no strategy preference; between subject) and with prompt type (i.e., focused vs. open; within-subject), assessing learning performance, cognitive load, attention allocation, quantity and quality of explanation, and behavioral patterns. Study results showed that, compared to students using open prompts and with no self-explanation preference, providing focused prompts improved their learning performance and explanation quality, lowering their cognitive load and enabling them to search for information more accurately. Meanwhile, for students with a self-explanation preference, the two types of prompts used in this study had a similar positive impact on their learning performance and their quality of explanation.

Self-regulated learning (SRL) significantly impacts the process and outcome of programming problem-solving . Studies on SRL behavioural patterns of programming students based on trace data are limited in number and lack of coverage. In this study, hence, the Hidden Markov Model (HMM) was employed to probabilistically mine trace data from a visual programming learning platform, intending to unveil students’ SRL states and patterns during programming problem-solving in a bottom-up manner. Furthermore, the K-means clustering technique was utilized to cluster the Online Self-regulated Learning Questionnaire (OSLQ) survey data, enabling the investigation of prominent behavioural characteristics and patterns among students with differing levels of SRL. The results show that programming problem-solving involves five SRL states: problem information processing, task decomposition and planning, goal-oriented knowledge reconstruction, data modelling and solution formulating. Students with a high level of SRL are more engaged in the problem information processing stage, where they plan task objectives and develop problem-solving strategies by profoundly analyzing the structural relationships of the problem. In contrast, students with low levels of SRL decompose the problem and develop a strategic approach through interacting with the knowledge content, which results in a certain blindness in the problem-solving process.

With the increasing bandwidth, videos have been gradually used as submissions for online peer assessment activities. However, their transient nature imposes a high cognitive load on students, particularly low-ability students. Therefore, reviewers' ability is a key factor that may affect the reviewing process and performance in an online video peer assessment activity. This study examined how reviewers' ability affected the comments they provided and their reviewing behaviors and performance. Thirty-eight first-year undergraduate students participated in an online video peer assessment activity for 3 weeks. This study analyzed data collected from the teacher's and peer reviewers' ratings, comments provided by peer reviewers, and system logs. Several findings are significant. First, low-ability reviewers preferred to rate higher scores than high-ability reviewers did. Second, low-ability reviewers had higher review errors than high-ability reviewers. Third, high-ability reviewers provided more high-level comments, while low-ability reviewers provided more low-level comments. Finally, low- and high-ability reviewers showed different behavior patterns when reviewing peers' videos. In particular, low-ability reviewers invested more time and effort in understanding video content, while high-ability reviewers invested more time and effort in detecting and diagnosing problems. These findings are discussed, and several suggestions for improving the instructional and system design of online video peer assessment activities are provided.

Learning programming has become increasingly popular, with learners from diverse backgrounds and experiences requiring different support. Programming-process analysis helps to identify solver types and needs for assistance. The study examined students’ behavior patterns in programming among beginners and non-beginners to identify solver types, assess midterm exam scores’ differences, and evaluate the types’ persistence. Data from Thonny logs were collected during introductory programming exams in 2022, with sample sizes of 301 and 275. Cluster analysis revealed four solver types: many runs and errors, a large proportion of syntax errors, balance in all features, and a late start with executions. Significant score differences were found in the second midterm exam. The late start of executions characterizes one group with lower performance, and types are impersistent during the first programming course. The findings underscore the importance of teaching debugging early and the need to teach how to program using regular executions.

Wheel-spinning is unproductive persistence without the mastery of skills. Understanding wheel-spinning during the use of intelligent tutoring systems (ITSs) is crucial to help improve productivity and learning. In this study, following Beck and Gong (2013), we defined wheel-spinning students (unsuccessful students in ITSs) as those who practiced the same skill set over 10 times but failed to submit correct answers three times in a row. The t-SNE and K-means clustering algorithms were used to probe wheel-spinning learning patterns. Our results showed three types of wheel-spinning patterns when using ASSISTments, an online mathematics tutoring system. The findings indicate that a lack of motivation, math knowledge, or metacognitive ability can cause the failure to learn math with ITSs, which provides us with a deeper understanding of students' failure in ITSs and clues about how we can help these unsuccessful students in ITSs.

Massive Open Online Courses (MOOCs) have gradually become a dominant trend in education. Since 2014, the Ministry of Education in Taiwan has been promoting MOOC programs, with successful results. The ability of students to work at their own pace, however, is associated with low MOOC completion rates and has recently become a focus. The development of a mechanism to effectively improve course completion rates continues to be of great interest to both teachers and researchers. This study established a series of learning behaviors using the video clickstream records of students, through a MOOC platform, to identify seven types of cognitive participation models of learners. We subsequently built practical machine learning models by using K-nearest neighbor (KNN), support vector machines (SVM), and artificial neural network (ANN) algorithms to predict students’ learning outcomes via their learning behaviors. The ANN machine learning method had the highest prediction accuracy. Based on the prediction results, we saw a correlation between video viewing behavior and learning outcomes. This could allow teachers to help students needing extra support successfully pass the course. To further improve our method, we classified the course videos based on their content. There were three video categories: theoretical, experimental, and analytic. Different prediction models were built for each of these three video types and their combinations. We performed the accuracy verification; our experimental results showed that we could use only theoretical and experimental video data, instead of all three types of data, to generate prediction models without significant differences in prediction accuracy. In addition to data reduction in model generation, this could help teachers evaluate the effectiveness of course videos.

The aim of the study was to investigate how psychosocial problems in childhood and adolescence associate with an unhealthy health behavior pattern among adolescents in Northern Finland. The study population consisted of 4350 participants, drawn from the Northern Finland Birth Cohort 1986 Study. Health behavior patterns were assessed in adolescence and psychosocial problems in childhood and adolescence. Logistic regression analyses were performed to determine the associations. Several psychosocial problems predicted greater likelihood of engaging in unhealthy health behavior pattern. Externalizing problems in childhood predicted greater likelihood of engaging in unhealthy behavior patterns for girls. For both genders, externalizing problems and inattention in adolescence were associated with unhealthy health behavior patterns. Boys and girls with externalizing problems both in childhood and adolescence had an increased risk of unhealthy patterns. Psychosocial problems contribute to unhealthy lifestyles and should therefore be acknowledged when designing and targeting health promotion strategies aimed at adolescents.