To explore the impact of quantified self on individual learning performance, a multi-situation experimental design was employed to conduct comparative studies on learning activities. The study focused on analyzing the differential effects of quantified self under different goal settings. Learning activities were tracked through reading tracking, drawing measurement, and answering on an online platform. Results reveal that, without a goal, quantified self improves individual learning outcomes but detracts from learning experience, with no effect on willingness to relearn. It also promotes the selection of high-effectiveness learning categories but reduces creativity seeking in learning. With a goal, quantified self reduces individual learning outcomes but enhances learning experience and increases willingness to relearn. It also reduces the selection of high-effectiveness learning categories but enhances creativity seeking in learning. This study offers valuable implications for educators and learners in optimizing learning strategies and experiences through the use of quantified self techniques.

Online fundraising with group interaction and information sharing transforms traditional individual private donation information into group-visible information, which further affects the online donation behaviour of individuals receiving donation information from others in the fundraising platform. Based on the type of donation information from others and the relationship strength between individuals and others, this study analyses the changes in the donation intention of individuals receiving beneficial or damaging donation information from others with different levels of relationship strength. The results of multi-situation simulation experiments reveal that, the beneficial donation information of others with weak (strong) relationship strength will arouse individuals’ low (high) group identity, making their online donation amount close to (far exceeding) the platform’s recommended donation benchmark, with strong (weak) donation autonomy. The damaging donation information of others with weak (strong) relationship strength will arouse individuals’ low (high) deontic justice, making their online donation amount less than (far exceeding) the platform’s recommended donation benchmark, with weak (strong) donation autonomy. Research conclusions further enrich the theory of public welfare behaviour from the online context and group level, and provide inspiration for emerging online platforms to optimise the design of fundraising activities and promote individual rational donations.

We investigate the problem of determining the trajectory that an observer should follow to be able to accurately track a target in a bearings-only measurements context. We assume that the target's motion is uniform and that the measurements are corrupted by an additive Gaussian white noise. Though, in theory, this process is observable if the observer maneuvers with turns or accelerations, the quality of the resulting estimation strongly depends on the trajectory chosen by the observer. In this paper, we present a numerical method to compute a trajectory of a maneuvering observer with the objective of maximizing the cumulative sum of bearing rates between the target and observer. Our approach is based on the piecewise stochastic control of a finite-horizon Markov process. A quantization method is applied to transform the problem into a discrete domain. We show that this transformation allows for a numerically tractable solution able to accurately track the target in a number of practical scenarios.