Programming courses report high failure rates, particularly among students who rely on surface-level pattern recognition rather than developing conceptual understanding. While large language models offer problem generation automation, they present limitations including unpredictable quality and limited instructor control. This paper proposes a domain-specific language (DSL) that extracts generation strategies from LLMs and formalizes them into a controllable system for creating diverse programming exercises. Our DSL uses XML-style tags in code comments to specify probabilistic transformations while preserving instructor oversight. We conducted a quasi-experimental study with 86 students comparing DSL-generated exercises against static problems. Results showed significant overall improvement (p = 0.0186), with notable benefits for lower-performing students (p = 0.0230) but no difference for higher performers. Post-study interviews revealed that DSL diversity forces students to abandon superficial pattern recognition while automating accurate conceptual understanding. These findings suggest systematic problem diversity effectively supports struggling learners by preventing rote memorization and encouraging genuine code comprehension.

This paper presents a new technique for creating machine-learning exercise materials using spreadsheets. Spreadsheets are often used in teaching machine learning for beginners and non-specialists. However, computation in machine learning models is divided into a learning phase and an inference phase, and conventional spreadsheet-based materials either rely on the software's extensions for the learning phase or can result in huge sheets, which is unsuitable for learners who want to observe it. We realize the learning phase on a spreadsheet that fits within a PC screen, including visualization for better understanding, without using any extensions. Also, this implementation technique makes it possible to execute or pause the learning process interactively. We will show an example of a neural network implementation and discuss the merits and limitations of this technique.

As it is becoming more common for various handheld devices to be equipped with a camera, applications that use image recognition are increasing. Therefore, improving the usability of these devices by enhancing their image capture characteristics is becoming more important. We present ClearPlate - a new optical unit for image capture. It has a physically transparent viewfinder through which the user can observe the target directly without offset; the framed image is captured by the built-in camera. We also present a user study that evaluates ClearPlate performance in the 2D barcode acquisition task. Results show that ClearPlate significantly outperforms the camera-phone-based approach with regard to 2D barcode acquisition.

Capturing the user's vital signs is an urgent goal in the HCI community. Photo-plethysmography (PPG) is one approach; it can collect data from the finger tips that indicate the user's autonomic nervous system (ANS) and offers new potentials such as mental stress measurement and drowsy state detection. Our goal is to set PPG sensors on the surfaces of ordinary devices such as mice, smartphones, and steering wheels. This will offer smart monitoring without the burden of additional wearable sensors. Unfortunately, current PPG sensors are very narrow, and even if the sensor is attached to the surface of a device, the user is forced to align and hold the finger to the sensor point, which degrades device usability. To solve this problem, we propose an area-based sensing method that relaxes the alignment requirement. The proposed method uses two thin acrylic plates, a diffuser plate and a detection plate, as an IR waveguide. The proposed method can yield very thin sensing surfaces and gentle curvatures are possible. An experiment compares the proposed method to the conventional point-sensor in terms of LF/HF discrimination performance with the participant in the resting state, and the proposed method is shown to offer comparable sensing performance with superior usability.

Television sets and video game consoles equipped with a web browser have appeared, and we are now able to browse web pages on television screens. However, existing navigation techniques are too difficult in this situation. In this paper, we propose Arrow Tag, a new link selection technique for web browsers on TV. In this technique, sequences of arrow signs called Arrow Tags are assigned to the links of the web pages, so users can select the links by pushing the four direction keys a few times, while keeping her/his gaze fixed on the television screen. User studies show that Arrow Tag significantly outperforms the conventional techniques of Focus Move and Number Tag. Moreover, most participants preferred Arrow Tag over either Focus Move or Number Tag.

We conduct an experiment to investigate how interactive amusement media attract the users. For the experiment, we have developed the exploration oriented interactive amusement media, and show that users' behavior tends to segment every similar manipulation in the time scale. We also suggest the hypothesis that such a behavioral pattern is due to a series of taking further interest within the duration of an interest, and argue what forms of interactive amusement media cause such behavioral patterns.