Pauses and spacing in learning to program

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.Peer reviewe

Short pauses while studying considered harmful

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Comparison of Time Metrics in Programming

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.Peer reviewe

Using and Collecting Fine-Grained Usage Data to Improve Online Learning Materials

As educators seek to create better learning materials, knowledge about how students actually use the materials is priceless. The advent of online learning materials has allowed tracking of student movement on levels not previously possible with on-paper materials: server logs can be parsed for details on when students opened certain pages. But such data is extremely coarse and only allows for rudimentary usage analysis. How do students move within the course pages? What do they read in detail and what do they glance over? Traditionally, answering such questions has required complex setups with eye tracking labs. In this paper we investigate how fine-grained data about student movement within an online learning material can be used to improve said material in an informed fashion. Our data is collected by a JavaScript-component that tracks which elements of the online learning material are visible on the student's browser window as they study. The data is collected in situ, and no software needs to be installed on the student's computer. We further investigate how such data can be combined with data from a separate learning environment in which students work on course assignments and if the types of movements made by the students are correlated with student self-regulation metrics or course outcomes. Our results indicate that the use of rather simple and non-invasive tracking of students' movements in course materials allows material creators to quickly see major problem-areas in their materials and to highlight sections that students keep returning to. In addition, when the tracking data is combined with student course assignment data, inferring meaningful assignment-specific areas within the course material becomes possible. Finally, we determine that high-level statistics of user movements are not correlated with course outcomes or certain self-regulation related metrics.Peer reviewe

Sisäilmaongelmien ennaltaehkäisy elinkaarimallia käytettäessä ja energiatehokkuutta tavoiteltaessa (SEEK)

Suomen kunnat ja kaupungit ovat käyttäneet erilaisia hankintamuotoja uusiin kouluihin ja peruskorjausten tekemiseen kouluissa. Elinkaarimalli on yksi perinteisten hankintamuotojen rinnalle kehitetty malli, jossa palveluntuottaja yhdellä sopimuksella vastaa ainakin hankkeen suunnittelusta, rakentamisesta ja ylläpidosta pitkän sopimuskauden (10−40 vuoden) ajan. Tässä tutkimuksessa kerättiin informaatiota suomalaisista elinkaarikouluista ja verrattiin tuloksia kaupungin omarahoitteisiin kouluihin, joiden huolto ja ylläpito oli tuotettu kaupungin omalla organisaatiolla. Tutkimus tehtiin vuosina 2014‒2016 kuudessa elinaarikoulussa ja kuudessa kaupungin omarahoitteisessa koulussa. Sisäympäristön laatua arvioitiin mittauksin ja käyttäjäkokemuksin. Lisäksi kouluissa arvioitiin rakennusteknisten tekijöiden vaikutusta sisäilman laatuun asiakirjatietojen ja kohteessa tehtyjen katselmusten avulla. Myös koulujen energiakulutustietoja arvioitiin ja verrattiin hankkeissa käytettyjä elinkaarisopimuksien sisältöjä

Tiloja kehittämässä

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Tragedioita ja 350 kuppia kahvia – asiaa muutoksesta

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