diff --git a/app/assets/images/publications/zhidkikh-2024.png b/app/assets/images/publications/zhidkikh-2024.png
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Binary files /dev/null and b/app/assets/images/publications/zhidkikh-2024.png differ
diff --git a/app/assets/stylesheets/pages/publications.css.scss b/app/assets/stylesheets/pages/publications.css.scss
index f15fd5ee68..7d30bf0156 100644
--- a/app/assets/stylesheets/pages/publications.css.scss
+++ b/app/assets/stylesheets/pages/publications.css.scss
@@ -1,3 +1,7 @@
.publication-image {
max-width: inherit;
+
+ img {
+ max-width: inherit;
+ }
}
diff --git a/app/views/pages/publications.html.erb b/app/views/pages/publications.html.erb
index 44ed452a5b..560785bba6 100644
--- a/app/views/pages/publications.html.erb
+++ b/app/views/pages/publications.html.erb
@@ -4,6 +4,41 @@
<%= t '.title' %>
+
+
+
Reproducing Predictive Learning Analytics in CS1: Toward Generalizable and Explainable Models for Enhancing Student Retention
+
+ Denis Zhidkikh, Ville Heilala, Charlotte Van Petegem, Peter Dawyndt, Miitta Järvinen, Sami Viitanen, Bram De Wever, Bart Mesuere, Vesa Lappalainen, Lauri Kettunen, Raija Hämäläinen
+
+
+
+
+
+
Abstract
+
+ Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1).
+ General-purpose and interpretable dropout predictions still pose a challenge.
+ Our study aims to reproduce and extend the data analysis of a privacy-first student pass–fail prediction approach proposed by Van Petegem and colleagues (2022) in a different CS1 course.
+ Using student submission and self-report data, we investigated the reproducibility of the original approach, the effect of adding self-reports to the model, and the interpretability of the model features.
+ The results showed that the original approach for student dropout prediction could be successfully reproduced in a different course context and that adding self-report data to the prediction model improved accuracy for the first four weeks.
+ We also identified relevant features associated with dropout in the CS1 course, such as timely submission of tasks and iterative problem solving.
+ When analyzing student behaviour, submission data and self-report data were found to complement each other.
+ The results highlight the importance of transparency and generalizability in learning analytics and the need for future research to identify other factors beyond self-reported aptitude measures and student behaviour that can enhance dropout prediction.
+
+
Citation
+
+ Zhidkikh, D., Heilala, V., Van Petegem, C., Dawyndt, P., Järvinen, M., Viitanen, S., De Wever, B., Mesuere, B., Lappalainen, V., Kettunen, L., & Hämäläinen, R. (2024). Reproducing Predictive Learning Analytics in CS1: Toward Generalizable and Explainable Models for Enhancing Student Retention. Journal of Learning Analytics, 1-21. <%= link_to "https://doi.org/10.18608/jla.2024.7979", "https://doi.org/10.18608/jla.2024.7979" %>
+
+
+
+ <%= link_to "https://doi.org/10.18608/jla.2024.7979", class: "publication-image" do %>
+ <%= image_tag "publications/zhidkikh-2024.png" %>
+ <% end %>
+
+
+
+
Dodona: Learn to code with a virtual co-teacher that supports active learning
@@ -30,7 +65,9 @@
- <%= image_tag "publications/vanpetegem-2023-2.png", class: "publication-image" %>
+ <%= link_to "https://doi.org/10.1016/j.softx.2023.101578", class: "publication-image" do %>
+ <%= image_tag "publications/vanpetegem-2023-2.png" %>
+ <% end %>
@@ -64,7 +101,9 @@
- <%= image_tag "publications/maertens-2023.png", class: "publication-image" %>
+ <%= link_to "https://doi.org/10.1145/3587103.3594166", class: "publication-image" do %>
+ <%= image_tag "publications/maertens-2023.png" %>
+ <% end %>
@@ -95,7 +134,9 @@
- <%= image_tag "publications/vanpetegem-2023.png", class: "publication-image" %>
+ <%= link_to "https://doi.org/10.1145/3587103.3594165", class: "publication-image" do %>
+ <%= image_tag "publications/vanpetegem-2023.png" %>
+ <% end %>
@@ -129,7 +170,9 @@
- <%= image_tag "publications/strijbol-2023.png", class: "publication-image" %>
+ <%= link_to "https://doi.org/10.1145/3587103.3594189", class: "publication-image" do %>
+ <%= image_tag "publications/strijbol-2023.png" %>
+ <% end %>
@@ -162,7 +205,9 @@
- <%= image_tag "publications/strijbol-2022.png", class: "publication-image" %>
+ <%= link_to "https://doi.org/10.1016/j.softx.2023.101404", class: "publication-image" do %>
+ <%= image_tag "publications/strijbol-2022.png" %>
+ <% end %>
@@ -191,7 +236,9 @@
- <%= image_tag "publications/vanpetegem-2022.png", class: "publication-image" %>
+ <%= link_to "https://doi.org/10.1177/07356331221085595", class: "publication-image" do %>
+ <%= image_tag "publications/vanpetegem-2022.png" %>
+ <% end %>
@@ -223,7 +270,9 @@
- <%= image_tag "publications/maertens-2022.png", class: "publication-image" %>
+ <%= link_to "https://doi.org/10.1111/jcal.12662", class: "publication-image" do %>
+ <%= image_tag "publications/maertens-2022.png" %>
+ <% end %>