Motivation: Database courses focus on SQL querying (DQL) while treating schema definition (DDL) and data manipulation (DML) as side topics, even though real-world database work begins with understanding schema design and data updates. This misalignment leaves students underprepared for authentic data management practice. Method: We integrated scaffolded DDL and DML exercises as a core concept in a third-year data course across three offerings (2023-2025). Students completed structured weekly tasks in an LMS that provides immediate feedback and unlimited attempts, encouraging low-stakes, iterative practice. We analyzed student interaction data (number of attempts and performance) to examine learning patterns across DDL/DML and DQL. We analyzed 9,071 total exercise submissions from 669 students, examining both the number of LMS exercise attempts and assignment performance across DDL/DML and DQL. Results: Students required fewer attempts on DDL/DML tasks than on traditional DQL tasks, indicating strong receptiveness when these topics were properly scaffolded. Early performance on schema-definition tasks was moderately correlated with later SQL performance, suggesting that schema competence supports subsequent query learning. Implications: We encourage database educators to teach schema design and data manipulation as core topics to strengthen students' conceptual foundations, as our results suggest these skills are learnable with scaffolding and may support subsequent query learning.

General Game Playing (GGP) is a field of study in which artificial agents are required to compete in games whose rules are not known until runtime. In this domain, time is at a premium, as there may be less than a minute for an agent to initialize and decide on each of its actions. As a result, Monte Carlo Tree Search (MCTS) has been favoured by researchers in this domain for its ability to run in whatever time it is given, by quickly simulating many instances of a game. Heuristics may be used to guide these simulations, but since the game is not known in advance, a typical MCTS agent cannot know which heuristics are likely to be useful, and must expend precious time in trying to discover them. However, an agent able to take advantage of knowledge gained from prior experience with other, different, games, can do better. In this paper, we present a technique for automatically transferring heuristic knowledge between distinct, but similar, games. We show that using this knowledge to improve the quality of game-independent heuristics can produce better performance in games within the GGP framework, especially when initialization time is short, and we show that negative transfer is possible to detect and avoid.

In the field of General Game Playing (GGP), artificial agents (bots) may be required to play never-before-seen games with less than one minute to initialize and train. Although tabula rasa approaches, like Monte-Carlo Tree Search, are popular in this domain, they do not leverage information from the many different games that a bot has previously encountered. A major barrier to transfer learning has been the difficulty in identifying similar features in the rule descriptions of two different games. We present two methods, called MMap and LMap, for heuristically approximating a distance between two games' graphs, and producing a mapping for the symbols of one to the other, thereby enabling transfer. We evaluate the effectiveness of these methods across a variety of transfer scenarios, and find that both methods are far more accurate than a simpler baseline mapper. MMap is found to be more robust than LMap, but LMap is much faster, and so more suitable for general use in GGP.

Recent work established that it is possible for human artists to encode information into hand-drawn markers, but it is difficult to do when simultaneously maintaining aesthetic quality. We present two methods for relieving the mental burden associated with encoding, while allowing an artist to draw as freely as possible. A 'Helper Overlay' guides the artist with real-time feedback indicating where visual features should be added or removed, and an 'Autocomplete Tool' directly adds necessary features to the drawing for the artist to touch up. Both methods are enabled by a two-part algorithm that uses a tree-search for finding 'major' changes and a dynamic programming method for finding the minimum number of 'minor' changes. A 24-person study demonstrates that a majority of participants prefer both tools over previous methods of manual encoding, with the Helper Overlay being the more popular of the two.

Previous techniques for human-designable visual markers have focused on small encoding spaces, and assume artists do not need to encode specific bit representations. We present a general framework for human-designable visual markers for artists to encode specific bit representations in large spaces. A three-part study, conducted over three weeks, methodically evaluates the usability of different encoding methods when artists encode specific bit representations. The methods span different shape characteristics suitable for artist encoding (convexity, hollowness, number, size, and distance from centroid) and visualization tools are proposed to aid in this process. We further demonstrate that any of the methods presented may be practically used to encode a URL with the aid of a universally available database like TinyURL (rather than a task-specific database), making human-designable visual markers practical for applications such as advertisements.