Mapping Nanocellulose- and Alginate-Based Photosynthetic Cell Factory Scaffolds:Interlinking Porosity, Wet Strength, and Gas Exchange

To develop efficient solid-state photosynthetic cell factories for sustainable chemical production, we present an interdisciplinary experimental toolbox to investigate and interlink the structure, operative stability, and gas transfer properties of alginate- and nanocellulose-based hydrogel matrices with entrapped wild-type Synechocystis PCC 6803 cyanobacteria. We created a rheological map based on the mechanical performance of the hydrogel matrices. The results highlighted the importance of Ca2+-cross-linking and showed that nanocellulose matrices possess higher yield properties, and alginate matrices possess higher rest properties. We observed higher porosity for nanocellulose-based matrices in a water-swollen state via calorimetric thermoporosimetry and scanning electron microscopy imaging. Finally, by pioneering a gas flux analysis via membrane-inlet mass spectrometry for entrapped cells, we observed that the porosity and rigidity of the matrices are connected to their gas exchange rates over time. Overall, these findings link the dynamic properties of the life-sustaining matrix to the performance of the immobilized cells in tailored solid-state photosynthetic cell factories.</p

Framework for pedagogical learning analytics

Learning analytics is an emergent technological practice and a multidisciplinary scientific discipline, which goal is to facilitate effective learning and knowledge of learning. In this design science research, I combine knowledge discovery process, a concept of pedagogical knowledge, ethics of learning analytics and microservice architecture. The result is a framework for pedagogical learning analytics. The framework is applied and evaluated in the context of agency analytics. The framework contributes to the practical use of learning analytics

Learning analytics with learning and analytics : advancing student agency analytics

Pedagogically meaningful, research-based, and ethical learning analytics could foster the values and learning aims we want to advance in our society and educational system. However, it is essential to combine knowledge of the learning sciences and computational sciences when developing and applying learning analytics. This dissertation advances ananalytics approach called student agency analytics that utilizes learning analytics methods and computational psychometrics. Student agency is a vital characteristic of a learner, especially during times of uncertainty and change. Student agency has been raised to an important position in educational policymaking, and it has been identified as an essential aspect to consider when facilitating lifelong learning. The research advances the analysis process, examines the results from the student and teacher point of view, and provides novel insights into student agency. Specifically, the research addresses the issue of how to combine theoretical knowledge of learning and analytical methods as a comprehensive process in learning analytics while taking into account teachers’ perspectives, methodological issues, and some limitations in learning analytics. The results show that i) student agency can be characterized, and different profiles can be generated using robust clustering, ii) higher course satisfaction and performance is associated with higher student agency, iii) students reporting low agentic resources experience various restrictive aspects in learning, iv) explainable artificial intelligence techniques can provide additional insight about the intricacies of student agency, and v) teachers can utilize the analytics results in professional reflection and pedagogical decision-making.Pedagogisesti mielekkään, tutkimukseen perustuvan ja eettiset näkökulmat huomioon ottavan oppimisanalytiikan avulla on mahdollista edistääh aluamiamme arvoja ja tukea oppimistavoitteita. Oppimisanalytiikan kehittämisessä ja soveltamisessa on kuitenkin tärkeää yhdistää sekä oppimistieteiden että laskennallisten tieteiden tietoa ja osaamista. Tässä väitöskirjassa kehitetään opiskelijan toimijuusanalytiikkaa, joka hyödyntää sekä oppimisanalytiikan menetelmiä että laskennallista psykometriikkaa. Opiskelijan toimijuus on eräs keskeisistä käsitteistä koulutuspoliittisessa päätöksenteossa ja olennainen asia huomioida myös elinikäisessä oppimisessa. Tässä tutkimuksessa kehitetään opiskelijatoimijuuden analyysiprosessia, tarkastellaan analytiikkaa opiskelijan ja opettajan näkökulmasta sekä selvitetään toimijuuden yhteyksiä eri oppimiskokemuksiin. Tutkimuksessa tarkastellaan erityisesti sitä, miten oppimisteoreettinen tieto ja oppimisanalytiikka voidaan yhdistää ottaen samalla huomioon opettajannäkökulma, menetelmälliset kysymykset sekä oppimisanalytiikkaan liittyvät rajoitteet. Tutkimuksen tulokset osoittavat, että i) opiskelijan toimijuutta voidaan analysoida ja profiloida käyttämällä robustia klusterointia, ii) kurssityytyväisyys ja akateeminen suoriutuminen ovat yhteydessä opiskelijoiden toimijuuskokemuksiin, iii) alhaisimman toimijuusprofiilin opiskelijat kokevat erilaisten tekijöiden rajoittavan oppimistaan, iv) selitettävän tekoälyn menetelmät voivat antaa lisätietoa opiskelijoiden toimijuuteen liittyvistä kokemuksista, ja v) opettajat voivat hyödyntää analytiikan tuloksia ammatillisessa reflektiossa ja pedagogisessa päätöksenteossa

Course Satisfaction in Engineering Education Through the Lens of Student Agency Analytics

This Research Full Paper presents an examination of the relationships between course satisfaction and student agency resources in engineering education. Satisfaction experienced in learning is known to benefit the students in many ways. However, the varying significance of the different factors of course satisfaction is not entirely clear. We used a validated questionnaire instrument, exploratory statistics, and supervised machine learning to examine how the different factors of student agency affect course satisfaction among engineering students (N = 293). Teacher’s support and trust for the teacher were identified as both important and critical factors concerning experienced course satisfaction. Participatory resources of agency and gender proved to be less important factors. The results provide convincing evidence about the possibility to identify the most important factors affecting course satisfaction.peerReviewe

Student agency analytics : learning analytics as a tool for analysing student agency in higher education

This paper presents a novel approach and a method of learning analytics to study student agency in higher education. Agency is a concept that holistically depicts important constituents of intentional, purposeful, and meaningful learning. Within workplace learning research, agency is seen at the core of expertise. However, in the higher education field, agency is an empirically less studied phenomenon with also lacking coherent conceptual base. Furthermore, tools for students and teachers need to be developed to support learners in their agency construction. We study student agency as a multidimensional phenomenon centring on student-experienced resources of their agency. We call the analytics process developed here student agency analytics, referring to the application of learning analytics methods for data on student agency collected using a validated instrument. The data are analysed with unsupervised and supervised methods. The whole analytics process will be automated using microservice architecture. We provide empirical characterisations of student-perceived agency resources by applying the analytics process in two university courses. Finally, we discuss the possibilities of using agency analytics in supporting students to recognise their resources for agentic learning and consider contributions of agency analytics to improve academic advising and teachers' pedagogical knowledge.peerReviewe

Exploring Technology Readiness Among Finnish University Students

New technologies have the potential to support inclusive and collaborative learning processes. However, students’ technology readiness influences how they utilize learning technologies. This research examined technology readiness among Finnish university students (N = 796) utilizing Technology Readiness Index TRI 2.0, which showed promising psychometric properties in a student sample. Latent class analysis was used to obtain profiles with different characteristics. Our findings provide encouraging evidence that TRI 2.0 could be a valuable explanatory variable in modeling the use of educational technology.peerReviewe

Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures : Linking Hand Movement, Proximity, and Gaze Data

This study employed multimodal learning analytics (MMLA) to analyze behavioral dynamics during the ABCDE procedure in nursing education, focusing on gaze entropy, hand movement velocities, and proximity measures. Utilizing accelerometers and eye-tracking techniques, behaviorgrams were generated to depict various procedural phases. Results identified four primary phases characterized by distinct patterns of visual attention, hand movements, and proximity to the patient or instruments. The findings suggest that MMLA can offer valuable insights into procedural competence in medical education. This research underscores the potential of MMLA to provide detailed, objective evaluations of clinical procedures and their inherent complexities.peerReviewe

The Finnish Version of the Affinity for Technology Interaction (ATI) Scale : Psychometric Properties and an Examination of Gender Differences

The pervasiveness of technical systems in our lives calls for a broad understanding of the interaction between humans and technology. Affinity for technology interaction (ATI) scale measures the tendency of a person to actively engage or to avoid interaction with technological systems, including both software and physical devices. This research presents a psychometric analysis of a Finnish version of the ATI scale. The data consisted of 796 responses of students in a Finnish university. The data were analyzed utilizing factor analysis and both nonparametric and parametric item response theory. The Finnish version of the ATI scale proved to be essentially unidimensional, showing high reliability estimates, and forming a strong Mokken scale. Hierarchical multiple regression analysis showed that men had a slightly higher affinity for technology than women when controlling for age and field of study; however the effect size was small.peerReviewe

Explainable Student Agency Analytics

Several studies have shown that complex nonlinear learning analytics (LA) techniques outperform the traditional ones. However, the actual integration of these techniques in automatic LA systems remains rare because they are generally presumed to be opaque. At the same time, the current reviews on LA in higher education point out that LA should be more grounded to the learning science with actual linkage to teachers and pedagogical planning. In this study, we aim to address these two challenges. First, we discuss different techniques that open up the decision-making process of complex techniques and how they can be integrated in LA tools. More precisely, we present various global and local explainable techniques with an example of an automatic LA process that provides information about different resources that can support student agency in higher education institutes. Second, we exemplify these techniques and the LA process through recently collected student agency data in four courses of the same content taught by four different teachers. Altogether, we demonstrate how this process—which we call explainable student agency analytics—can contribute to teachers’ pedagogical planning through the LA cycle.peerReviewe