Open Player Modeling: Empowering Players through Data Transparency

Data is becoming an important central point for making design decisions for most software. Game development is not an exception. As data-driven methods and systems start to populate these environments, a good question is: can we make models developed from this data transparent to users? In this paper, we synthesize existing work from the Intelligent User Interface and Learning Science research communities, where they started to investigate the potential of making such data and models available to users. We then present a new area exploring this question, which we call Open Player Modeling, as an emerging research area. We define the design space of Open Player Models and present exciting open problems that the games research community can explore. We conclude the paper with a case study and discuss the potential value of this approach

A Comparison of the Performance of Neural Q-learning and Soar-RL on a Derivative of the Block Design (BD)/Block Design Multiple Choice (BDMC) Subtests on the WISC-IV Intelligence Test

Teaching an autonomous agent to perform tasks that are simple to humans can be complex, especially when the task requires successive steps, has a low likelihood of successful completion with a brute force approach, and when the solution space is too large or too complex to be explicitly encoded. Reinforcement learning algorithms are particularly suited to such situations, and are based on rewards that help the agent to find the optimal action to execute given a certain state. The task investigated in this thesis is a modified form of the Block Design (BD) and Block Design Multiple Choice (BDMC) subtests, used by the Fourth Edition of the Wechsler Intelligence Scale for Children (WISC-IV) to partially assess childrens\u27 learning abilities. This thesis investigates the implementation, training, and performance of two reinforcement learning architectures for this problem: Soar-RL, a production system capable of reinforcement learning, and a Q-learning neural network. The objective is to help define the advantages and disadvantages of solving problems using these architectures. This thesis will show that Soar is intuitive for implementation and is able to find an optimal policy, although it is limited by its execution of exploratory actions. The neural network is also able to find an optimal policy and outperforms Soar, but the convergence of the solution is highly dependent on the architecture of the neural network

The effect of synthesizing strategy to students’ reading comprehension at XI grade SMA N 1 Panyabungan

This research focused on the effect of using Synthesizing Strategy on Students‟ Reading Comprehension Ability at Grade XI SMA N 1 Panyabungan. The problems of this research students were lazy to read, students had lack motivation in reading, also students got difficulties in reading even though have read in many years. The purpose of this research was to find out the effect of using Synthesizing strategy on students‟ reading comprehension ability at grade XI SMA N 1 Panyabungan. This research employed experimental research. The population of this research was XI Sains grade of SMA N 1 Panyabungan. The total of population were fourth classes. Then, the sample of the research was 2 classes, experimental class (XI MIPA-3) and control class (XI MIPA-2). It was taken cluster sampling after conducting normality and homogeneity test. To collect the data, researcher used test for measuring students‟ reading comprehension ability. To analysis the data, the researcher used T-test. Based on the result of the research, researcher showed the description of the data was found that mean score of pre-test in experimental class was higher than control class (52.8>50.13). Then, after using Synthesizing strategy the result of mean score post-test experimental class was higher than control class (86.04>78.4), and the score of tcount was bigger than ttable (3.056>2.000). It means that hyphothesis alternative (Ha) was accepted. It was concluded that there was significant effect of using Synthesizing Strategy at Grade XI SMA N 1Panyabungan

An assessment of critical thinking in the Middle East: Evaluating the effectiveness of special courses interventions.

Critical thinking is a requisite skill for college success, employability, and conducive active civic participation. Empirical studies have noted to the low achievement of Arab students on critical thinking assessments. Insufficient endeavors have attempted to propose effective interventions enhancing critical thinking abilities among Arab students. The current analysis provides a preliminary overview of a special course designed to improve critical thinking skills among Arab college students. Results indicated a great improvement in all areas of critical thinking including explanation of information, identification of strategies, implementing solutions, and formulating logical inferences. Students' scores on a critical thinking assessment increased from sufficient to good as a result of participating in the program. The gains are consistent after controlling for gender, major, class seniority, and nationality. Notwithstanding these promising results, this paper is limited in several respects including the choice of critical thinking assessments represented by two questions, the highly contextualized setting making it difficult to be replicated, and the convenient sampling strategy used to recruit participants. This set of limitations, however, does not discourage proactive attempts like designing special courses to enhance students' critical thinking acquisition in the Middle East

Explaining Aha! moments in artificial agents through IKE-XAI: Implicit Knowledge Extraction for eXplainable AI

During the learning process, a child develops a mental representation of the task he or she is learning. A Machine Learning algorithm develops also a latent representation of the task it learns. We investigate the development of the knowledge construction of an artificial agent through the analysis of its behavior, i.e., its sequences of moves while learning to perform the Tower of Hanoï (TOH) task. The TOH is a well-known task in experimental contexts to study the problem-solving processes and one of the fundamental processes of children’s knowledge construction about their world. We position ourselves in the field of explainable reinforcement learning for developmental robotics, at the crossroads of cognitive modeling and explainable AI. Our main contribution proposes a 3-step methodology named Implicit Knowledge Extraction with eXplainable Artificial Intelligence (IKE-XAI) to extract the implicit knowledge, in form of an automaton, encoded by an artificial agent during its learning. We showcase this technique to solve and explain the TOH task when researchers have only access to moves that represent observational behavior as in human–machine interaction. Therefore, to extract the agent acquired knowledge at different stages of its training, our approach combines: first, a Q-learning agent that learns to perform the TOH task; second, a trained recurrent neural network that encodes an implicit representation of the TOH task; and third, an XAI process using a post-hoc implicit rule extraction algorithm to extract finite state automata. We propose using graph representations as visual and explicit explanations of the behavior of the Q-learning agent. Our experiments show that the IKEXAI approach helps understanding the development of the Q-learning agent behavior by providing a global explanation of its knowledge evolution during learning. IKE-XAI also allows researchers to identify the agent’s Aha! moment by determining from what moment the knowledge representation stabilizes and the agent no longer learns.Region BretagneEuropean Union via the FEDER programSpanish Government Juan de la Cierva Incorporacion - MCIN/AEI IJC2019-039152-IGoogle Research Scholar Gran

3CAM MODEL CONCEPT MAPS, CRITICAL THINKING, COLLABORATION ASSESSMENT (3CAM) TOWARD THE PATH OF MASTERY LEARNING

This study reports an exploratory study of the 3CAM model of classroom learning. 3CAM is an acronym for concept maps, critical thinking, collaboration, and mastery. It is a student-centered approach to mastery learning that empowers students to take responsibility for their own learning. The model is a theory and a practice. The theory is the language games of critical thinking and the practice is the activities of visualizing concept maps, applying critical thinking, collaborating, and creating their own assessment. Students play the language games of critical thinking using the WH questions: “what, when, why, where, who and how”. Students apply the model each week to the chapters of a child development text. The study also compared two groups of students: a group working collaboratively and a group working individually using the 3CAM model. The results of the study support the practices of the activities of the model as well as the theory of the language games of critical thinking. The data reveal that students who work collaboratively use significantly more “why, how and when” questions in creating their concept maps. The most used critical thinking question was “what”, and its use declined in the collaborative group as the use of “why, how and when” increased. The use of “what” remained the same for the individual group. Student comments about the model were so supportive of both theory and practice

“It’s Dangerous to Go Alone”: An Autoethnography of College English Students Reading Video Games as Texts

My dissertation research studies the use of video games as texts for analysis in a College English course. The purpose of the study was to see what happens when College English students are asked to engage with a video game as a class text, use their engagement with a video game to make sense of other texts, and how reader-response theory applies to making meaning of video games as texts. A secondary purpose was to study, if this transaction does take place, whether video games can support the kind of analysis required of a College English curriculum and what this curriculum might look like. I conducted this study as an autoethnography of a course designed for this purpose as the course instructor. Observing my students’ participation and analyzing their written work served as the primary data, as well as self-reflection on my own meaning-making processes. My final observations suggest that students engaged with the video game as a class text, though not more than they might have any other text; however, the nature of playing the text (and the multiple interpretations that afforded individual students) encouraged a critical reading in which students readily participated. For this reason, game choice was of paramount importance, that it might align with learning objectives but was accessible to a wide variety of prior experience with video games. Finally, a committee of department faculty deemed the majority of student work as of the quality expected for the course, suggesting video games can serve as texts for analysis that the field expects of its students. The implications of this study should inform English Education’s adaption to teaching the multiple literacies of the 21st century, as this research itself is multimodal and requires multiple literacies to read. This choice of research method and format was also meant to serve as examples of the transactions I and students experienced in the study

Earthquake: Game-Based Learning for 21st Century STEM Education

To play is to learn. A lack of empirical research within game-based learning literature, however, has hindered educational stakeholders to make informed decisions about game-based learning for 21st century STEM education. In this study, I modified a research and development (R&D) process to create a collaborative-competitive educational board game illuminating elements of earthquake engineering. I oriented instruction- and game-design principles around 21st century science education to adapt the R&D process to develop the educational game, Earthquake. As part of the R&D, I evaluated Earthquake for empirical evidence to support the claim that game-play results in student gains in critical thinking, scientific argumentation, metacognitive abilities, and earthquake engineering content knowledge. I developed Earthquake with the aid of eight focus groups with varying levels of expertise in science education research, teaching, administration, and game-design. After developing a functional prototype, I pilot-tested Earthquake with teacher-participants (n=14) who engaged in semi-structured interviews after their game-play. I analyzed teacher interviews with constant comparison methodology. I used teachers’ comments and feedback from content knowledge experts to integrate game modifications, implementing results to improve Earthquake. I added player roles, simplified phrasing on cards, and produced an introductory video. I then administered the modified Earthquake game to two groups of high school student-participants (n = 6), who played twice. To seek evidence documenting support for my knowledge claim, I analyzed videotapes of students’ game-play using a game-based learning checklist. My assessment of learning gains revealed increases in all categories of students’ performance: critical thinking, metacognition, scientific argumentation, and earthquake engineering content knowledge acquisition. Players in both student-groups improved mostly in critical thinking, having doubled the number of exhibited instances of critical thinking between games. Players in the first group exhibited about a third more instances of metacognition between games, while players in the second group doubled such instances. Between games, players in both groups more than doubled the number of exhibited instances of using earthquake engineering content knowledge. The student-players expanded use of scientific argumentation for all game-based learning checklist categories. With empirical evidence, I conclude play and learning can connect for successful 21st century STEM education