The general goal of automated speech assessment (ASA) is to provide a consistent and objective evaluation on the spoken language proficiency of an L2 learner or test-taker. In contrast to most previous work that treats ASA as a nominal multi-classification task and thus neglects the sequential nature of proficiency grades, this paper explores the notion of soft labels for use in ASA. In particular, we strive to enhance ASA performance by examining two critical issues: (1) the impact of applying soft labels instead of hard labels in the optimization of ordinal classification for ASA, and (2) the effects of combining self-supervised learning (SSL) with handcrafted indicator features via a novel modeling paradigm. Our results demonstrate that the proposed model can considerably enhance performance compared to existing strong baselines. The improvement is evident not only in the test dataset of seen prompts but also in those of unseen prompts, suggesting the robust generalization and adaptability of our method.

The ubiquity of self-tracking devices and smartphone apps has empowered people to collect data about themselves and try to self-improve. However, people with little to no personal analytics experience may not be able to analyze data or run experiments on their own (self-experiments). To lower the barrier to intervention-based self-experimentation, we developed an app called Self-E, which guides users through the experiment. We conducted a 2-week diary study with 16 participants from the local population and a second study with a more advanced group of users to investigate how they perceive and carry out self-experiments with the help of Self-E, and what challenges they face. We find that users are influenced by their preconceived notions of how healthy a given behavior is, making it difficult to follow Self-E’s directions and trusting its results. We present suggestions to overcome this challenge, such as by incorporating empathy and scaffolding in the system.

Planning a solution before writing code is essential in algorithmic problem-solving. However, novices often skip planning and jump straight into coding. Even if they set up a plan, some do not connect to their plan when writing code. Learners solving algorithmic problems often struggle with high-level components such as solution techniques and sub-problems, but existing representations that guide learners in planning, such as flowcharts, focus on presenting lower-level details. We use subgoal diagrams -- diagrams made of subgoal labels and the relationships between them -- as a representation that guides learners to focus on high-level plans when they develop solutions. We introduce AlgoPlan, an interface that enables learners to build their own subgoal diagram and use it to guide their problem-solving process. A preliminary study with seven students shows that subgoal diagrams help learners focus on high-level plans and connect these plans to their code.