Meaningful Learning in the Fields of Engineering and ICT-Business Higher Education
Heilala, Janne (2021)
2021
Kasvatuksen ja yhteiskunnan tutkimuksen maisteriohjelma - Master´s Programme in Educational Studies
Kasvatustieteiden ja kulttuurin tiedekunta - Faculty of Education and Culture
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Hyväksymispäivämäärä
2021-07-01
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202105044426
https://urn.fi/URN:NBN:fi:tuni-202105044426
Tiivistelmä
This learning artefact is an interpretation of multidimensional learning experiences obtained from academic students during a new virus (COVID-19) spread over the globe. The virus forced institutions to close the facilities for a fast timely shift to distance learning. Concurrently to exceptional circumstances, datasets obtained included a total of N = 78 sample. The total set was divided into cohort I (n = 44) and cohort II (n = 34) at the level of lower tertiary level. Quantitative data with Statistical Package for Social Sciences (SPSS) processing was linking to teaching-studying-learning (TSL) process factors in convergence to qualitative concepts. Measurable process concepts were Extrinsic Motivation and Success (EMS), Intrinsic Motivation and Success (IMS), Interaction within Learning Environment (ILE), Learning Beliefs (LBs), and Meaningful Learning (ML).
Sum variables were created from each latent to be modeled pairwise. Sum variables were rotated through Confirmatory Factor Analyses (CFAs) to check latent loadings and reliabilities. Both CFA and Cronbach’s α’s validated for good reliableness. Pairwise correlation modeling was a default setting. Process heated up to the linear regression (LR) analyses. Try was to find answers for hypothesized predictions based on the research questions: whether the loadings are at a good level, do they correlate, and do they predict significantly each other as theory would dictate.
As a result, cohort I perceptions were at a good level by IMS and LBs; with slightly negatively agreeing to disagree between EMS, ILE, & ML; statistically significant correlations with CFA were only found between domain-specific motivations against LBs and ML; perceptions with LR angled positively, but ILE and ML were having alarming negative predictor levels indicating slightly to Rote Learning (RL) from a complement for ML. 2nd part of the cross-sectional study included construable cohort II perceptions, which were heading to a very good level by IMS, while on averagely, EMS, ILE, LBs, and ML were beautifully over good level; with statistically very significant embedded correlations; and strongly positive regressor predictions over examined sum variables. As a result, the study suggests that pains can be reduced during competitive COVID-19, as students' perception levels within TSL can be heavily lifted independently from the exceptional pandemic situation.
Sum variables were created from each latent to be modeled pairwise. Sum variables were rotated through Confirmatory Factor Analyses (CFAs) to check latent loadings and reliabilities. Both CFA and Cronbach’s α’s validated for good reliableness. Pairwise correlation modeling was a default setting. Process heated up to the linear regression (LR) analyses. Try was to find answers for hypothesized predictions based on the research questions: whether the loadings are at a good level, do they correlate, and do they predict significantly each other as theory would dictate.
As a result, cohort I perceptions were at a good level by IMS and LBs; with slightly negatively agreeing to disagree between EMS, ILE, & ML; statistically significant correlations with CFA were only found between domain-specific motivations against LBs and ML; perceptions with LR angled positively, but ILE and ML were having alarming negative predictor levels indicating slightly to Rote Learning (RL) from a complement for ML. 2nd part of the cross-sectional study included construable cohort II perceptions, which were heading to a very good level by IMS, while on averagely, EMS, ILE, LBs, and ML were beautifully over good level; with statistically very significant embedded correlations; and strongly positive regressor predictions over examined sum variables. As a result, the study suggests that pains can be reduced during competitive COVID-19, as students' perception levels within TSL can be heavily lifted independently from the exceptional pandemic situation.