Cultural heritage conservation has two aspects, tangible and intangible, both of which contribute greatly to the understanding of ancient inheritances. Due to the role of education in the preservation process, and the strength of the new media in the current era, serious games can play a key role in conservancy by transmitting the target culture. There is a gap in the serious game field in relation to Turkey's cultural heritage on the Silk Roads, underlining the motivation of this research. Hence, (...) this study proposes the Anatolian Journey serious game, which is developed in the Twine platform, designed to transmit Turkey's tangible and intangible cultural heritage, providing comprehensive information on the Seljuk caravanserais, located on the Silk Roads. Moreover, the research compares undergraduate and graduate students' gains in knowledge of heritage data while playing a serious game and encountering the same content in text form with an online survey. (shrink)

Aging is accompanied by changes in general cognitive functioning which may impact the learning rate of older adults; however, this is often not controlled for in cognitive aging studies. We investigated the contribution of differences in learning rates to age-related differences in landmark knowledge acquired from route learning. In Experiment 1 we used a standard learning procedure in which participants received a fixed amount of exposure to a route. Consistent with previous research, we found age-related (...) deficits in associative cue and landmark sequence knowledge. Experiment 2 controlled for differences in learning rates by using a flexible exposure learning procedure. Specifically, participants were trained to a performance criterion during route learning before being tested on the content of their route knowledge. While older adults took longer to learn the route than younger adults, the age-related differences in associative cue knowledge were abolished. The deficit in landmark sequence knowledge, however, remained. Experiment 3 replicated these results and introduced a test situation in which a deficit in landmark sequence knowledge yielded an increased likelihood of disorientation in older adults. The findings of this study suggest that age-related deficits in landmark associative cue knowledge are attenuated by controlling for learning rates. In contrast, landmark sequence knowledge deficits persist and are best explained by changes in the learning strategy of older adults to acquire task essential associative cue knowledge at the expense of supplementary sequence knowledge. (shrink)

Throughout history, education has always been essential for humanity's justice and fundamental for the creation of a free and satisfying society with the dissemination of knowledge. Hence, in addition to the life occurrences educating people, traditional higher education methods have played an important role for a long period. However, the age of technology has changed the educational system along with the people's lifestyles to meet the continuously changing conditions. During the past twenty years, the Information and Communication Technologies (ICTs) led (...) to the emergence of e-learning non-traditional educational methods bringing about an innovative dimension of virtual learning. With its beneficial features provided by the Internet and various technologies, this method passes the control of time and location from the tutors to the students during the learning process. Despite the entrance of online education into the students' life many years ago, the Covid-19 outbreak initiated in 2020 turned this method into the only possible way of education for almost two years. Afterward, plenty of studies investigated its effects on students' social and academic life, mostly considering its negative impacts on their mental health and motivation due to interaction issues. They also pointed out the specific problems of tactile learner students in the lack of face-to-face design courses. As an innovative solution able to be adopted, game technologies, increasing immersion of the learner in a real-world skill, and triggering motivation are one of the best methods integrated into educational systems. Known as serious games, these games, adopted by almost any domain, convey educational content along with entertainment, increasing interaction. They train or enhance a certain skill or topic to the player by immersing them. While serious games are more expanded nowadays, they are still not being played by the mass public due to people's immersiveness expectations of a game. Hence, the Head-Mounted Displays (HMDs), with their emergence, increased the players' engagement and immersion with all types of virtual environments in digital games. This technology, however, evolved serious games area most, among others, by enabling the opportunity to provide a riskless and low-cost learning environment for real-world skills. In the process of suggesting serious games as a replacement for the traditional educational methods, the most important factor is being aware of their effectiveness for education. Generally, digital games, one of the world's biggest and fastest-growing industries, rely heavily on the virtual landscape, a major part of its development process. Its design demands remarkable effort, and it directly affects the whole game's characteristics. Hence, serious games, as a type of digital game, carry the same attributes. With the appearance of virtual reality technology, gaming started to gain more immersiveness and engagement with artificial elements and environments. Although virtual landscapes were always being developed since the emergence of digital games, the studies taking virtual landscapes into account are mostly using them as a tool for enhancing real spaces, not as a domain for the sake of its development. While other design domains are applying various design methodologies, it is challenging to find a standard design methodology for the design and production of virtual landscapes in the gaming industry, leading to inconsistent and low-quality results. Hence, it comes to the question of what is the virtual landscapes' role in serious games and their effect on the data comprehension rate. Since the literature lacks regarding the gradual change of the virtual landscape in serious games and the content they transmit, this thesis aims to figure out the gradual chronological evolution of the virtual landscapes in serious games. It also aims to seek how this change and the virtual reality technology invention have affected the contents transmitted by digital games and its influence on the player interaction and learning rate. To do so, in the process of this thesis, we developed, tested, and evaluated various serious games, both text-based and with 360° photos. The steps taken revealed the reluctance of the players to follow the providing educational content and their inadequacy of interaction. Hence, the thesis seeks the reasons ending to this result and the ways of increasing this learning rate. Afterward, the thesis uses a currently available but not massively used digital game classification methodology. This methodology, which is based on the principles of architecture, landscape architecture, and urban planning, classifies the games based on their constituting virtual landscapes. Hence, the thesis considers all Steam games, one of the biggest video game digital distribution services, filtered with the 'education' tag, ending in 2531 digital games. Afterward, adding the 'game' tag the result reduced to 1102 games. Finally, with some manual filterings ignoring the irrelevant content, the final number decreased to 702. Sorting chronologically, we made an Excel database including the games' introductory information, the content they transmit, their release date, if they support HMD or not, and whether they are simulation games or not. Due to Steam's dating spectrum, our database includes games dating back from 1992 to 2020. Afterward, we classified all of these games using the virtual landscape classification methodology and generated their related codes based on their player scale, story, dimension, space shape, and interaction level. Investigating the results, they revealed some growth patterns relating to the content educated in the game, the VR technology, and the virtual landscape evolution. Adding the user reviews of Steam, we reverse engineered five of the best serious games on the database due to the digital game design methodology. This methodology divides the games' virtual environment into five layers, namely, player activity map (PAM), story layer, natural environment layer, virtual environment layer, and media layer. Reverse engineering the five best digital games ever, based on the IGN database, enabled us to compare the outcomes. Due to released data, we understand how the games' assets are distributed within the various layers of the virtual environment in the most successful games. Finally, the thesis aims to provide a framework based on these results for developing serious games to increase player interaction and enhance the learning rate. However, these results can be expanded to any virtual landscape other than only serious games' for their interaction enhancement. Currently, the landscape architecture domain only adopts the virtual landscape area as a tool for enhancing design in the real world. However, in the near future, landscape architects will perform in the virtual landscape area as a domain to be enhanced. Hence, that day, the results of this study will play an important role and will be a roadmap for generating highly interactive virtual landscapes. (shrink)

Causal structure learning from time series data is a major scientific challenge. Existing algorithms assume that measurements occur sufficiently quickly; more precisely, they assume that the system and measurement timescales are approximately equal. In many scientific domains, however, measurements occur at a significantly slower rate than the underlying system changes. Moreover, the size of the mismatch between timescales is often unknown. This paper provides three distinct causal structure learning algorithms, all of which discover all dynamic graphs that (...) could explain the observed measurement data as arising from undersampling at some rate. That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic. We apply these algorithms to data from simulations. The results provide insight into the challenge of undersampling. (shrink)

An extension to a rating system for tracking the evolution of parameters over time using continuous variables is introduced. The proposed rating system assumes a distribution for the continuous responses, which is agnostic to the origin of the continuous scores and thus can be used for applications as varied as continuous scores obtained from language testing to scores derived from accuracy and response time from elementary arithmetic learning systems. Large-scale, high-stakes, online, anywhere anytime learning and testing inherently comes (...) with a number of unique problems that require new psychometric solutions. These include (1) the cold start problem, (2) problem of change, and (3) the problem of personalization and adaptation. We outline how our proposed method addresses each of these problems. Three simulations are carried out to demonstrate the utility of the proposed rating system. (shrink)

As machine learning informs increasingly consequential decisions, different metrics have been proposed for measuring algorithmic bias or unfairness. Two popular “fairness measures” are calibration and equality of false positive rate. Each measure seems intuitively important, but notably, it is usually impossible to satisfy both measures. For this reason, a large literature in machine learning speaks of a “fairness tradeoff” between these two measures. This framing assumes that both measures are, in fact, capturing something important. To date, philosophers (...) have seldom examined this crucial assumption, and examined to what extent each measure actually tracks a normatively important property. This makes this inevitable statistical conflict – between calibration and false positive rate equality – an important topic for ethics. In this paper, I give an ethical framework for thinking about these measures and argue that, contrary to initial appearances, false positive rate equality is in fact morally irrelevant and does not measure fairness. (shrink)

Previous research suggests that early performance of amnesic individuals in a probabilistic category learning task is relatively unimpaired. When combined with impaired declarative knowledge, this is taken as evidence for the existence of separate implicit and explicit memory systems. The present study contains a more fine-grained analysis of learning than earlier studies. Using a dynamic lens model approach with plausible learning models, we found that the learning process is indeed indistinguishable between an amnesic and control group. (...) However, in contrast to earlier findings, we found that explicit knowledge of the task structure is also good in both the amnesic and the control group. This is inconsistent with a crucial prediction from the multiple-systems account. The results can be explained from a single system account and previously found differences in later categorization performance can be accounted for by a difference in learning rate. (shrink)

Book detailsBlum, S.. Ungrading: Why rating students undermines learning. West Virginia University Press.

We analyze naturally occurring datasets from student use of educational technologies to explore a long-standing question of the scope of transfer of learning. We contrast a faculty theory of broad transfer with a component theory of more constrained transfer. To test these theories, we develop statistical models of them. These models use latent variables to represent mental functions that are changed while learning to cause a reduction in error rates for new tasks. Strong versions of these models provide (...) a common explanation for the variance in task difficulty and transfer. Weak versions decouple difficulty and transfer explanations by describing task difficulty with parameters for each unique task. We evaluate these models in terms of both their prediction accuracy on held-out data and their power in explaining task difficulty and learning transfer. In comparisons across eight datasets, we find that the component models provide both better predictions and better explanations than the faculty models. Weak model variations tend to improve generalization across students, but hurt generalization across items and make a sacrifice to explanatory power. More generally, the approach could be used to identify malleable components of cognitive functions, such as spatial reasoning or executive functions. (shrink)

The application of machine learning algorithms to healthcare data can enhance patient care while also reducing healthcare worker cognitive load. These algorithms can be used to detect anomalous physiological readings, potentially leading to expedited emergency response or new knowledge about the development of a health condition. However, while there has been much research conducted in assessing the performance of anomaly detection algorithms on well-known public datasets, there is less conceptual comparison across unsupervised and supervised performance on physiological data. Moreover, (...) while heart rate data are both ubiquitous and noninvasive, there has been little research specifically for anomaly detection of this type of data. Considering that heart rate data are indicative of both potential health complications and an individual’s physical activity, this is a rich source of largely overlooked data. To this end, we employed and evaluated five machine learning algorithms, two of which are unsupervised and the remaining three supervised, in their ability to detect anomalies in heart rate data. These algorithms were then evaluated on real heart rate data. Findings supported the effectiveness of local outlier factor and random forests algorithms in the task of heart rate anomaly detection, as each model generalized well from their training on simulated heart rate data to real world heart rate data. Furthermore, results support that simulated data can help configure algorithms to a degree of performance when real labeled data are not available and that this type of learning might be especially helpful in initial deployment of a system without prior data. (shrink)

We extend recent information-theoretic phase transition approaches to evolutionary and cognitive process via the Rate Distortion and Joint Asymptotic Equipartition Theorems, in the circumstance of interaction with a highly structured environment. This suggests that learning plateaus in cognitive systems and punctuated equilibria in evolutionary process are formally analogous, even though evolution is not cognitive. Extending arguments by Adami et al. (2000), we argue that 'adaptation' is the process by which a distorted genetic image of a coherently structured environment (...) is imposed upon a species. (shrink)

Self-relevance has been demonstrated to impair instrumental learning. Compared to unfamiliar symbols associated with a friend, analogous stimuli linked with the self are learned more slowly. What is not yet understood, however, is whether this effect extends beyond arbitrary stimuli to material with intrinsically meaningful properties. Take, for example, stimulus valence an established moderator of self-bias. Does the desirability of to-be-learned material influence self-learning? Here, in conjunction with computational modelling (i.e. Reinforcement Learning Drift Diffusion Model analysis), a (...) probabilistic selection task was used to establish if and how stimulus valence (i.e. desirable/undesirable posters) impacts the acquisition of knowledge relating to object-ownership (i.e. owned-by-self vs. owned-by-friend). Several interesting results were observed. First, undesirable posters were learned more rapidly for self compared to friend, an effect that was reversed for desirable posters. Second, learning rates were accompanied by associated differences in reward sensitivity toward desirable and undesirable choice selections as a function of ownership. Third, decisional caution was greater for self-relevant (vs. friend relevant) responses. Collectively, these findings inform understanding of self-function and how valence and stimulus relevance mutually influence probabilistic learning. (shrink)

Background: The proprioceptive neuromuscular facilitation reciprocal contraction pattern has the potential to increase the maximum rate of torque development. However, it is a more complex resistive exercise task and may interfere with improvements in the maximum rate of torque development due to motor skill learning, as observed for unidirectional contractions. The purpose of this study was to examine the cost-benefit of using the PNF exercise technique to increase the maximum rate of torque development.Methods: Twenty-six participants completed (...) isometric maximal extension-to-flexion or flexion-only contractions at the wrist. Ten of the assigned contractions were performed on each of three sessions separated by 48-h for skill acquisition. Retention was assessed with 5 contractions performed 2-weeks after acquisition. Torque and surface electromyographic activity were analyzed for evidence of facilitated contractions between groups, as well as alterations in muscle coordination assessed across test sessions. The criterion measures were: mean maximal isometric wrist flexion toque; the maximal rate of torque development ; root-mean-square error variability of the rate of torque versus torque phase-plane; the rate of wrist flexion muscle activation ; a coactivation ratio for wrist flexor and extensor sEMG activity; and wrist flexor electromechanical delay.Results: There were no significant differences between groups with respect to maximal wrist flexion torque, dτ/dtmax or RMSE variability of torque trajectories. Both groups exhibited a progressive increase in maximal strength and in dτ/dtmax from the start of acquisition to retention. RMSE was lowest after a 2-week rest interval. There were no significant differences between groups in the rate of muscle activation or the coactivation ratio. There was a reduction in coactivation that was retained after a 2-week rest interval. Alternatively, EMD was significantly greater in the experimental group across all sessions. However, both groups had a similar pattern of improvement to the third consecutive day of testing, but returned close to baseline value after the 2-week rest interval.Discussion: The wrist extension-to-flexion contraction pattern did not result in a greater maximal rate of torque development than simple contractions of the wrist flexors. There was no difference between groups with respect to motor skill learning. The main adaptation in neuromotor control was a decrease in coactivation, not the maximal rate of muscle activation. (shrink)

Even if one can experiment on relevant factors, learning the causal structure of a dynamical system can be quite difficult if the relevant measurement processes occur at a much slower sampling rate than the “true” underlying dynamics. This problem is exacerbated if the degree of mismatch is unknown. This paper gives a formal characterization of this learning problem, and then provides two sets of results. First, we prove a set of theorems characterizing how causal structures change under (...) undersampling. Second, we develop an algorithm for inferring aspects of the causal structure at the “true” timescale from the causal structure learned from the undersampled data. Research on causal learning in dynamical contexts has largely ignored the challenges of undersampling, but this paper provides a framework and foundation for learning causal structure from this type of complex time series data. (shrink)