Dashboards: Double Dash Paper

@article{Herodotou2025,

title = {A Participatory Approach to Designing a Student-Facing Dashboard for Online and Distance Education},

author = {Christothea Herodotou and Sagun Shrestha and Catherine Comfort and Heshan Andrews and Paul Mulholland and Vaclav Bayer and Claire Maguire and John Lee and Miriam Fernandez},

url = {https://doi.org/10.18608/jla.2025.8481},

doi = {10.18608/jla.2025.8481},

issn = {1929-7750},

year  = {2025},

date = {2025-07-08},

urldate = {2025-07-01},

journal = {Journal of Learning Analytics},

volume = {12},

number = {2},

pages = {158–174},

abstract = {In this paper, we explore the design of a student-facing dashboard for online and distance learning with a focus on capturing and addressing specific learning needs. A participatory process involving 20 students was employed, which included a screening questionnaire and focus group discussions. The selection of data points to be displayed on the dashboard was mainly determined by student responses regarding the usefulness of a feature, and a high frequency of their agreement. The data analysis revealed that the learning needs of online students relate to course support and communication (with tutors and other students). In response to this, students expressed a desire for accessing information related to their assignments, study time, and tutorials. The data points endorsed by students related to descriptive (assignment scores, engagement with the virtual learning environment, material accessed), predictive (score prediction), and prescriptive data (material recommendations and contact information). Student choices of data points were driven by a desire to better understand their study progress and take appropriate action. These insights emphasize the need for designing dashboards that not only describe performance but foremost “prescribe” to students potential solutions to overcome performance challenges.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Luebber2025b,

title = {Lottery before peer review is associated with increased female representation and reduced estimated economic cost in a German funding line},

author = {Finn Luebber and Sören Krach and Frieder M. Paulus and Lena Rademacher and Rima-Maria Rahal},

url = {https://doi.org/10.1038/s41467-025-65660-9},

doi = {10.1038/s41467-025-65660-9},

issn = {2041-1723},

year  = {2025},

date = {2025-12-00},

urldate = {2025-12-00},

journal = {Nature Communications},

volume = {16},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Research funding is a key determinant of scientific progress. However, current allocation procedures for third-party funding are criticized due to high costs and biases in the selection. Here, we present data from a large German funding organization on an implementation of a lottery-first approach followed by peer review to allocate funding. We examine the changes in submissions and funded projects of female applicants after implementation, estimate the costs of the overall allocation process, and report on the attitudes and satisfaction of researchers and reviewers. The data show an increase of 10% in submissions and a 23% increase in funded projects from female applicants with the lottery-first approach compared to a previously used procedure. Additionally, the lottery-first approach was estimated to have 68% lower economic costs compared to a conventional single-stage peer review approach. Satisfaction with this funding approach was high and around half of applicants preferred an initial lottery followed by peer review over a conventional approach. Thus, the lottery-first approach is a promising addition to allocation procedures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}