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Many games feature a progression of levels that doesn't adapt to the player. This can be problematic because some players may get stuck if the progression is too difficult, while others may find it boring if the progression is too slow to get to more challenging levels. This can be addressed by building levels based on the player's performance and preferences. In this work, we formulate the problem of generating levels for a player as a Markov Decision Process (MDP) and use adaptive dynamic programming (ADP) to solve the MDP before assembling a level. We tested with two case studies and found that using an ADP outperforms two baselines. Furthermore, we experimented with player proxies and switched them in the middle of play, and we show that a simple modification prior to running ADP results in quick adaptation. By using ADP, which searches the entire MDP, we produce a dynamic progression of levels that adapts to the player.

An increasingly common area of study in procedural content generation is the creation of level segments: short pieces that can be used to form larger levels. Previous work has used basic concatenation to form these larger levels. However, even if the segments themselves are completable and well-formed, concatenation can fail to produce levels that are completable and can cause broken in-game structures (e.g. malformed pipes in Mario). We show this with three tile-based games: a side-scrolling platformer, a vertical platformer, and a top-down roguelike. Additionally, we present a Markov chain and a tree search algorithm that finds a link between two level segments, which uses filters to ensure completability and unbroken in-game structures in the linked segments. We further show that these links work well for multi-segment levels. We find that this method reliably finds links between segments and is customizable to meet a designer's needs.

This report summarizes major findings from an independent evaluation of 2020 census operations. The American Statistical Association 2020 Census Quality Indicators Task Force selected the authors to conduct the evaluation using nonpublic operations data provided by the Census Bureau. The evaluation focused on the quality of state-level population counts released by the Census Bureau for congressional apportionment. The authors first partitioned the census enumeration process into five operation phases. Within each phase, one or more activities considered to be critical to census data quality were identified. Operational data from each activity were then analyzed to assess the risk of error, particularly as they related to similar activities in the 2010 census. Overall, the evaluation found that census operations relied on higher risk activities at a higher rate in 2020 than in 2010, suggesting that the risk of error may be higher in 2020 than in 2010. However, the available data were insufficient to determine whether the apportionment counts are of lower quality in 2020 than in 2010.

Reflection is a critical aspect of the learning process. However, educational games tend to focus on supporting learning concepts rather than supporting reflection. While reflection occurs in educational games, the educational game design and research community can benefit from more knowledge of how to facilitate player reflection through game design. In this paper, we examine educational programming games and analyze how reflection is currently supported. We find that current approaches prioritize accuracy over the individual learning process and often only support reflection post-gameplay. Our analysis identifies common reflective features, and we develop a set of open areas for future work. We discuss these promising directions towards engaging the community in developing more mechanics for reflection in educational games.

Asserts that much of the emphasis on educational reform has focused on better teacher preparation. Argues that teaching is a life-long learning activity and good teachers must work to become better. Describes the rationale and procedures of the advanced teacher certification program of the National Council for Social Studies. (CFR)

Discusses the causes and consequences of shyness and presents the Shyness Control Program, a systematic approach for managing social anxiety in children and adolescents. Includes a flowchart that serves as a framework for the system and represents proven clinical methods used in anxiety management. (JAC)