Despite increasing amounts of data and ever improving natural language generation techniques, work on automated journalism is still relatively scarce. In this paper, we explore the field and challenges associated with building a journalistic natural language generation system. We present a set of requirements that should guide system design, including transparency, accuracy, modifiability and transferability. Guided by the requirements, we present a data-driven architecture for automated journalism that is largely domain and language independent. We illustrate its practical application in the production of news articles upon a user request about the 2017 Finnish municipal elections in three languages, demonstrating the successfulness of the data-driven, modular approach of the design. We then draw some lessons for future automated journalism.

Conventional wisdom holds that time is an integral part of the learning process. Spacing out learning over multiple study sessions seems to be better for learning than having a single longer study session. Learners should also take pauses from the learning process to absorb, assimilate, and analyze what they have just learned. At the same time, pausing too often can be harmful for learning. Participants of two subsequent introductory programming courses completed programming tasks in an integrated development environment that saved detailed logs of their actions, including time stamps of all the participants' keypresses in said environment. Using this data with background variables and a self-regulation metric questionnaire, we study how the students space out their work, identify trends in between the kinds of pauses the participants took and the course outcomes, and their connection to background variables. Based on our research, students tend to space out their work, working on multiple days each week. In addition, a high relative amount of pauses of only a few seconds correlated positively with exam scores, while a high relative amount of pauses of a few minutes correlated negatively with exam scores. Student pausing behaviors are poorly explained by traditional self-regulation measures such as the Motivated Strategies for Learning Questionnaire and other background variables.

Research on the indicators of student performance in introductory programming courses has traditionally focused on individual metrics and specific behaviors. These metrics include the amount of time and the quantity of steps such as code compilations, the number of completed assignments, and metrics that one cannot acquire from a programming environment. However, the differences in the predictive powers of different metrics and the cross-metric correlations are unclear, and thus there is no generally preferred metric of choice for examining time on task or effort in programming.In this work, we contribute to the stream of research on student time on task indicators through the analysis of a multi-source dataset that contains information about students' use of a programming environment, their use of the learning material as well as self-reported data on the amount of time that the students invested in the course and per-assignment perceptions on workload, educational value and difficulty. We compare and contrast metrics from the dataset with course performance. Our results indicate that traditionally used metrics from the same data source tend to form clusters that are highly correlated with each other, but correlate poorly with metrics from other data sources. Thus, researchers should utilize multiple data sources to gain a more accurate picture of students' learning.

In the 21st century, the skills of computational thinking complement those of traditional math teaching. In order to gain the knowledge required to teach these skills, a cohort of math teachers participated in an in-service training scheme conducted as a massive open online course (MOOC). This paper analyses the success of this training scheme and uses the results of the study to focus on the skills of computational thinking, and to explore how math teachers expect to integrate computing into the K-12 math syllabus. The coursework and feedback from the MOOC course indicate that they readily associate computational thinking with problem solving in math. In addition, some of the teachers are inspired by the new opportunities to be creative in their teaching. However, the set of programming concepts they refer to in their essays is insubstantial and unfocused, so these concepts are consolidated here to form a hypothetical learning trajectory for computational thinking.

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