The ÄlyOppi Project, funded by the Ministry of Education in 2018, aims at developing and extending e-learning environments within university education in Finland. The project Lääkelaskenta (in English, medication calculation) is part of ÄlyOppi, and its purpose is to improve the mathematical proficiency of students within various health care professions. In order to achieve this goal, we develop and evaluate novel e-learning materials hosted in the automated assessment system STACK for general mathematical content.

In this report, we describe the outcome of the first set of STACK-based exercises in Arcada University of Applied Sciences. In particular, we describe nursing students’ opinions, experiences, and students’ answer data from the pilot experiments.

Ultimately, the design of the STACK exercises will allow carrying out learning analytics on acquired data according to the 4 Cs teaching model by Johnson et al.. The 4 Cs model improves students’ Perceived Self-Efficacy (PSE) which is an important indicator related to social learning theory by Bandura. More precisely, PSE refers to the students belief in herself, and her ability to learn and to successfully carry out a task. Moreover, PSE is connected to motivation to learn and to ask for advice. There is a significant relationship between nursing students’ ability to perform dosage calculation, mathematics self-efficacy, and computer-assisted instruction received as pointed out by Hodge in 2002. Several research studies report low levels of proficiency in dosage calculation both for nursing students and registered nurses as pointed out in Dahl et al..

Mathematical skills in healthcare professions belong to the field of basic arithmetics: i.e., addition, subtraction, multiplication, division, and elementary applications such as linear equations. Also, logical reasoning and deduction are expected to be learned already in primary school. The true challenge in medication calculation is that required proficiency must be reached without any mistakes at all. Accordingly, a competency level as high as 99 % will lead to an unacceptable outcome and harm to patients sooner or later.

The aim of collecting and analysing learning data is to create evidence for improving and developing these e-learning materials. The long-term goal is to develop a modern STACK-based e-learning environment and gamified learning materials that allow automatized assessment and categorization of the students’ answers using, e.g., deep neural networks (DNN) and data mining.

The proposed e-learning solution (i) enables healthcare students to practise dosage calculations either independently or under the guidance of a teacher, (ii) helps students who require more practice, and (iii) is usable for nurses who wish to enhance their professional skills in dosage calculations for recertifying their competency. The strength of the solution is the capability of providing immediate feedback and even instructing the student based on identifying and categorizing the occurred errors.