Tapio Salakoski

Augmented Reality (AR) has been found to produce significant effects on individual learning outcomes but its impact on collaborative applications remains moderate. Existing AR frameworks emphasize individual instructional design, whereas frameworks for collaborative learning rarely engage with the spatial and device-mediated affordances of mobile AR. In response to this inadequacy in the literature, we introduce the Mobile Augmented-Reality Storytelling for Vocational Education and Training (MARS-VET) framework, a four-dimensional conceptual architecture that integrates Computer-Supported Collaborative Learning (CSCL) scripting principles with mobile AR affordances for collaborative English as a Foreign Language (EFL) writing in Vocational Education and Training (VET) settings. MARS-VET synthesizes theoretical perspectives across four dimensions: contextual anchoring, which embeds activities within authentic workplace scenarios; collaborative orchestration, which structures group interaction through macro- and micro-level scripts; competency cultivation, which sequences writing progression from model-based reproduction toward autonomous professional text production; and capacity building, which addresses the professional-development requirements of implementing educators. Content validity was established through expert panel evaluation involving international specialists (N = 11) who rated the framework against 36 items using a four-point relevance scale and provided additional qualitative feedback. The Scale-level Content Validity Index (S-CVI/Ave = 0.91) exceeded established thresholds, with all four dimensions achieving satisfactory item-level indices. Experts reached unanimous agreement on items addressing workplace scenario identification and co-located access to linguistic resources. Qualitative feedback led to terminology refinements and clarification of orchestration mechanisms. The framework offers VET institutions and educators a reference for the design and evaluation of collaborative AR experiences in an area where integrative frameworks have so far been lacking.

Over the past decade, the demand for automated protein function prediction has increased due to the volume of newly sequenced proteins. In this paper, we address the function prediction task by developing an ensemble system automatically assigning Gene Ontology (GO) terms to the given input protein sequence. We develop an ensemble system which combines the GO predictions made by random forest (RF) and neural network (NN) classifiers. Both RF and NN models rely on features derived from BLAST sequence alignments, taxonomy and protein signature analysis tools. In addition, we report on experiments with a NN model that directly analyzes the amino acid sequence as its sole input, using a convolutional layer. The Swiss-Prot database is used as the training and evaluation data. In the CAFA3 evaluation, which relies on experimental verification of the functional predictions, our submitted ensemble model demonstrates competitive performance ranking among top-10 best-performing systems out of over 100 submitted systems. In this paper, we evaluate and further improve the CAFA3-submitted system. Our machine learning models together with the data pre-processing and feature generation tools are publicly available as an open source software at https://github.com/TurkuNLP/CAFA3.

Background: Up to 35% of nurses’ working time is spent on care documentation. We describe the evaluation of a system aimed at assisting nurses in documenting patient care and potentially reducing the documentation workload. Our goal is to enable nurses to write or dictate nursing notes in a narrative manner without having to manually structure their text under subject headings. In the current care classification standard used in the targeted hospital, there are more than 500 subject headings to choose from, making it challenging and time consuming for nurses to use. Methods: The task of the presented system is to automatically group sentences into paragraphs and assign subject headings. For classification the system relies on a neural network-based text classification model. The nursing notes are initially classified on sentence level. Subsequently coherent paragraphs are constructed from related sentences. Results: Based on a manual evaluation conducted by a group of three domain experts, we find that in about 69% of the paragraphs formed by the system the topics of the sentences are coherent and the assigned paragraph headings correctly describe the topics. We also show that the use of a paragraph merging step reduces the number of paragraphs produced by 23% without affecting the performance of the system. Conclusions: The study shows that the presented system produces a coherent and logical structure for freely written nursing narratives and has the potential to reduce the time and effort nurses are currently spending on documenting care in hospitals.

In this study we investigate the effects of long-term technology enhanced learning (TEL) in mathematics learning performance and fluency, and how technology enhanced learning can be integrated into regular curriculum. The study was conducted in five second grade classes. Two of the classes formed a treatment group and the remaining three formed a control group. The treatment group used TEL in one mathematics lesson per week for 18 to 24 months. Other lessons were not changed. The difference in learning performance between the groups tested using a post-test; for that, we used a mathematics performance test and a mathematics fluency test. The results showed that the treatment group using TEL got statistically significantly higher learning performance results compared to the control group. The difference in arithmetic fluency was not statistically significant even though there was a small difference in favor of the treatment group. However, the difference in errors made in the fluency test was statistically significant in favor of the treatment group.

Over the past decade, the demand for automated protein function prediction has increased due to the volume of newly sequenced proteins. In this paper, we address the function prediction task by developing an ensemble system automatically assigning Gene Ontology (GO) terms to the given input protein sequence. We develop an ensemble system which combines the GO predictions made by random forest (RF) and neural network (NN) classifiers. Both RF and NN models rely on features derived from BLAST sequence alignments, taxonomy and protein signature analysis tools. In addition, we report on experiments with a NN model that directly analyzes the amino acid sequence as its sole input, using a convolutional layer. The Swiss-Prot database is used as the training and evaluation data. In the CAFA3 evaluation, which relies on experimental verification of the functional predictions, our submitted ensemble model demonstrates competitive performance ranking among top-10 best-performing systems out of over 100 submitted systems. In this paper, we evaluate and further improve the CAFA3-submitted system. Our machine learning models together with the data pre-processing and feature generation tools are publicly available as an open source software at https://github.com/TurkuNLP/CAFA3 Author summary: Understanding the role and function of proteins in biological processes is fundamental for new biological discoveries. Whereas modern sequencing methods have led to a rapid growth of protein databases, the function of these sequences is often unknown and expensive to determine experimentally. This has spurred a lot of interest in predictive modelling of protein functions. We develop a machine learning system for annotating protein sequences with functional definitions selected from a vast set of predefined functions. The approach is based on a combination of neural network and random forest classifiers with features covering structural and taxonomic properties and sequence similarity. The system is thoroughly evaluated on a large set of manually curated functional annotations and shows competitive performance in comparison to other suggested approaches. We also analyze the predictions for different functional annotation and taxonomy categories and measure the importance of different features for the task. This analysis reveals that the system is particularly efficient for bacterial protein sequences.