Obstructive sleep apnea (OSA) is a sleep disorder that causes partial or complete cessation of breathing during an individual's sleep. Various methods have been proposed to automatically detect OSA events, but little work has focused on predicting such events in advance, which is useful for the development of devices that regulate breathing during a patient's sleep. We propose four methods for sleep apnea prediction based on convolutional and long short-term memory neural networks (1D-CNN, ConvLSTM, 1D-CNN-LSTM and 2D-CNN-LSTM), which use raw data from three respiratory signals (nasal flow, abdominal and thoracic) sampled at 32 Hz, without any human-engineered features. We predict OSA (apnea or hypopnea) and normal breathing events 30 seconds ahead using the prior 90 seconds' data. Our results on a dataset containing over 46,000 examples from 1,507 subjects show that all four models achieved promising accuracy ( 81%). The 1D-CNN-LSTM and 2D-CNN-LSTM were the best two performing models with accuracy, sensitivity and specificity over 83%, 81% and 85% respectively. These results show that OSA events can be accurately predicted in advance based on respiratory signals, opening up opportunities for the development of devices to preemptively regulate the airflow to sleepers to avoid these events. Furthermore, we demonstrate good prediction performance even when respiratory signals are downsampled by a factor of 32, to 1 Hz, for which our proposed 1D-CNN-LSTM achieved 82.94% accuracy, 81.25% sensitivity and 84.63% specificity. This robustness to low sampling frequencies allows our algorithms to be implemented in devices with low storage capacity, making them suitable for at-home environments.

Programming problems are commonly used as a learning and assessment activity for learning to program. We believe that programming problems can be effective for broader learning goals. In our large-enrolment course, we have designed special programming problems relevant to logic, discrete mathematics, and the theory of computation, and we have used them for formative and summative assessment. In this report, we reflect on our experience. We aim to leverage our students' programming backgrounds by offering a code-based formalism for our mathematical syllabus. We find we can translate many traditional questions into programming problems of a special kind - calling for 'programs' as simple as a single expression, such as a formula or automaton represented in code. A web-based platform enables self-paced learning with rapid contextual corrective feedback, and helps us scale summative assessment to the size of our cohort. We identify several barriers arising with our approach and discuss how we have attempted to negate them. We highlight the potential of programming problems as a digital learning activity even beyond a logic and computation course.

Online programming courses can provide detailed automatic feedback for code that fails to meet various test conditions, but novice students often struggle with syntax errors and are unable to write valid testable code. Even for very simple exercises, the range of incorrect code can be surprising to educators with mastery of a programming language. This research paper presents an analysis of the error messages from code run by students in an introductory Python~3 programming course, participated in by 8680 primary and high-school students from 680 institutions. The invalid programs demonstrate a wide diversity of mistakes: even for a one-line "Hello World!'' exercise there were 115 unique invalid programs. The most common errors are identified and compared to the topics introduced in the course. The most generic errors in selected exercises are investigated in greater detail to understand the underlying causes. While the majority of students attempting an exercise reach a successful outcome, many students encounter at least one error in their code. Of these, many such errors indicate basic mistakes, such as unquoted string literals, even in exercises late in the course for which some proficiency of earlier concepts is assumed. These observations suggest there is significant scope to provide greater reinforcement of students' understanding of earlier concepts.

Obstructive sleep apnea is a breathing disorder affecting 2-4% of the adult population. It is characterized by periods of reduced breathing (hypopnea) or no breathing (apnea). Several machine learning algorithms have been proposed to automatically classify sleep apnea events, but little work has been done on predicting such events in advance, which is important for the treatment of sleep apnea, and especially for the development of auto-adjusting airway pressure devices to maintain continuous airflow during sleep. In this paper, we propose three methods for predicting sleep apnea events, based on convolution neural networks and Markov chains. Specifically, we use data from respiratory signals (nasal flow, abdominal and thoracic) to predict apnea and hypopnea events in a 30-second period using the prior 60 seconds' data. We evaluate the performance of the proposed methods for automatically learning the required features and predicting the sleep apnea events on a large dataset containing 48,000 examples from 1,507 subjects. The results show the effectiveness of the proposed convolutional neural network method, which achieved accuracy of 80.78% and F1 score of 80.63%. We also analyse the Markov chain rules and provide an overview of the transitions between apnea and normal events.

The SQL query language is the 'lingua franca' of transactional databases, and is essential for scalable data analytics. Learning SQL requires practical exercises with databases, including those parts of SQL with side-effects such as DDL statements, triggers, and stored procedures. Teaching this effectively to a non-technical audience is a challenge, especially in times of COVID-19 without face-to-face classes. The Grok Learning platform allows to design self-paced online tutorials with auto-graded exercises - but it was originally built for teaching programming languages. In this demo, we show how we extended the Grok platform to teach SQL for Data Scientists with comprehensive online learning. Grok supports a rich user interface with interactive examples where students can explore and experiment with each example query. This is ideal for learning declarative querying. Each query is executed in its own sandbox on a freshly initialised database instance which allows to teach all parts of SQL including DDL statements, stored procedures, triggers and UDFs. At the same time, the platform scales to thousands of concurrent users, while maintaining interactive response times.

Sleep apnea is a sleep disorder characterized by abnormal breathing patterns during sleep, and affecting 2-4% of the adult population. If left untreated, it increases the risk of heart attack, stroke, diabetes, depression and early death. There are two main types of abnormal breathing events: obstructive apnea and hypopnea. Detecting these events using the traditional machine learning approaches requires extraction and selection of suitable features from several respiratory channels, that are then used as inputs to a classification model. In this study, we present a new approach, based on convolutional neural networks, that automatically combines the raw data of three respiratory channels of polysomnography recordings (nasal airflow, thoracic and abdominal), without feature engineering, and classifies each 30s epoch as containing normal, obstructive apnea or hypopnea events. The evaluation was conducted on a large dataset from 1,507 subjects, containing 23,088 epochs from each of the three classes. We also studied the effectiveness of the individual channels for improving the classification accuracy by testing all channel combinations. Our results showed that the use of nasal airflow, thoracic and abdominal channels with a convolutional neural network was beneficial in detecting sleep apnea events. The combined use of the three channels outperformed all single and pair combinations of channels, achieving accuracy of 83.5%, which is sufficiently high for practical applications.