According to the previous blog, Lithuanian Intelligent Future School (IFS) project is aimed mainly at improving the quality and effectiveness of STEM education. According to IFS concept, after implementing integrated learner profiles, ontologies-based personalized recommender systems should be created to suggest learning components (learning objects, activities, methods, tools, apps etc.) suitable to particular learners according to their profiles. Ontologies and recommender systems should be based first of all on established interconnections between students’ learning styles and aforementioned learning components. While establishing those interconnections, pedagogically sound high-quality learning styles models and vocabularies of learning components should be applied, on the one hand, and experienced high-quality experts should participate in this work, on the other, thus applying group intelligence approach.
The first step to implement IFS is identification of students’ learning styles, and Felder-Silverman learning styles model (FSLSM) was used for this purpose. FSLSM is known as the most suitable for STEM and particularly for engineering education.
After that, the next IFS implementation step was organised i.e. creating interconnection of learners’ models with relevant learning activities. At this stage, inquiry based learning (IBL) activities were used because IBL is very suitable for STEM education.
Inquiry requires identification of assumptions, use of critical and logical thinking, and consideration of alternative explanations and scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work. Inquiry is referred to the science education literature to designate at least three distinct but interlinked categories of activity: (a) what scientists do when they use scientific methods, (b) how students learn (by pursuing scientific questions and engaging in scientific experiments by emulating the practices and processes used by scientists); and (c) a pedagogy, or teaching strategy, adopted by science teachers when they design learning activities, which allow students to observe, experiment and review what is known in light of evidence. In terms of learning, the inquiry-based approach is about engaging students’ curiosity in problems in the world and the ideas that surround them. In the workplace, this might mean observing and posing questions about situations. If their questions are too complex, they may try to simplify or model the situation. They may then try to answer their questions by collecting and analysing data, making representations and by developing connections to their existing knowledge. They then try to interpret their findings, checking that they are accurate and sensible, before sharing their findings with others. This process is often missing in the school classroom because the teacher often points out what must be observed, provides the questions, demonstrates the methods to be used and checks the results. Students are merely asked to follow the instructions.
This embraces several different approaches to inquiry-based instruction, including:
Structured inquiry — the teacher provides students with a hands-on problem to investigate, as well as the procedures, and materials, but does not inform them of expected outcomes. Students are to discover relationships between variables or otherwise generalize from data collected. These types of investigations are similar to those known as cookbook activities, although a cookbook activity generally includes more direction than a structured inquiry activity about what students are to observe and which data they are to collect.
Guided inquiry — the teacher provides only the materials and problem to investigate. Students devise their own procedure to solve the problem.
Open inquiry — this approach is similar to guided inquiry, with the addition that students also formulate their own problem to investigate. Open inquiry, in many ways, is analogous to doing science. Science fair activities are often examples of open inquiry.
Learning cycle — students are engaged in an activity that introduces a new concept. The teacher then provides the formal name for the concept. Students take ownership of the concept by applying it in a different context.
In order to interconnect FSLSM and IBL activities, a special questionnaire was created for Lithuanian teachers experts in the area. These teachers are experienced in personalized learning, and they participated in numerous training activities and international projects in the area. The questionnaire was created in Lithuanian using FSLSM and IBL activities and sub-activities vocabulary. The experts have been asked to fill in the questionnaire in terms of establishing suitability of proposed IBL activities A1-A10 and sub-activities (SA) A1.1-A9.2 to students’ learning styles according to FSLSM. The level of suitability have been proposed to express in linguistic variables ‘bad’, ‘poor’, ‘fair’, ‘good’ and ‘excellent’. After teachers experts had filled in the questionnaire, Vilnius University researchers have mapped linguistic variables into non-fuzzy values using trapezoidal fuzzy numbers to express the experts’ opinion in per cent.
The main results are as follows:
A1 ‘Orienting and asking questions’: A1.1. ‘Observe phenomena’ SA is mostly suitable to Active LS (86%) in comparison with Reflective LS (59%); A1.2. ‘Develop questions’ SA is also mostly fits Active LS (94%) in comparison with Reflective LS (83%); and A1.3. ‘Respond to questions’ SA mostly matches with Sensory LS (79%), and less – with Intuitive LS (62%).
A2 ‘Hypothesis generation’: A2.1. ‘Select and complete hypotheses’ SA is mostly suitable to Global LS (79%) in comparison with Sequential LS (75%); and A2.2. ‘State hypothesis’ SA mostly fits Active LS (75%) in comparison with Reflective LS (67%) and Visual LS (85%) vs Verbal (82%).
A3 ‘Planning’: A3.1. ‘Inquiry plan’ SA is mostly suitable to Active LS (86%) in comparison with Reflective LS (77%) and Intuitive LS (75%) vs Sensory (74%); A3.2. ‘Equipment and actions’ SA mostly fits Sensory LS (86%) in comparison with Intuitive LS (77%), Visual LS (88%) vs Verbal (86%), Active LS (88%) vs Reflective (72%), Sequential LS (87%) vs Global (70%); and A3.3. ‘Supported planning’ SA mostly matches with Sequential LS (91%), and less – with Global LS (85%).
A4 ‘Investigation’: A4.1. ‘Explore’ SA is mostly suitable to Intuitive LS (86%) in comparison with Sensory LS (78%), Visual LS (81%) vs Verbal (73%), Reflective LS (90%) vs Active (89%), Sequential LS (87%) vs Global (71%); A4.2. ‘Observe, conduct observation’ SA mostly fits Active LS (82%) in comparison with Reflective (80%); A4.3 ‘Experiment’ SA mostly fits Active LS (80%) vs Reflective (69%); and A4.4. ‘Organize data’ SA mostly matches with Sequential LS (89%) vs Global (75%) and Intuitive LS (70%) – vs Sensory (66%).
A5 ‘Analysis and interpretation’: A5.1. ‘Assess data’ SA is mostly suitable to Sensory LS (78%) in comparison with Intuitive (74%); A5.2. ‘Interpret data’ SA mostly fits Visual LS (84%) vs Verbal (82%); and A5.3. ‘Synthesize new knowledge’ SA mostly matches with Sequential LS (85%), and less – with Global (75%).
A6 ‘Model exploration and creation’: A6.1. ‘Discover’ SA is mostly suitable to Reflective LS (83%) in comparison with Active (79%); A6.2. ‘Develop’ SA mostly fits Active LS (84%) vs Reflective (79%); A6.3 ‘Evaluate’ SA mostly matches with Active LS (82%), and less – with Reflective (72%); and A6.4. ‘Expose’ SA mostly fits Visual LS (85%) vs Verbal (78%), Sensory LS (78%) vs Intuitive (71%), and Active LS (79%) – vs Reflective (69%).
A7 ‘Conclusion and evaluation’: A7.1. ‘Generalize the results’ SA is mostly suitable to Global LS (88%) in comparison with Sequential (79%); A7.2. ‘State conclusions’ SA mostly fits Active LS (87%) vs Reflective (82%); and A7.3. ‘Evaluate’ SA also mostly matches with Active LS (82%) vs Reflective (72%).
A8 ‘Communication and justifying’: A8.1. ‘Discuss’ SA is mostly suitable to Active LS (91%) in comparison with Reflective LS (77%); A8.2. ‘Share results’ SA mostly fits Active LS (92%) vs Reflective (70%); and A8.3. ‘Collaborate’ SA mostly matches with Active LS (91%), and less – with Reflective (72%).
A9 ‘Prediction’: A9.1. ‘Predict’ SA is mostly suitable to Active LS (88%) in comparison with Reflective LS (73%) and Sequential LS (82%) vs Global (80%); and A9.2. ‘Formulate further questions’ SA mostly fits Active LS (88%) vs Reflective (78%).
Thus, collective Experts’ intelligence is very helpful for teachers to establish suitable learning activities for their children according to children learning styles but only pedagogically sound well-established vocabularies of learning activities (such as IBL) and learning styles (such as FSLSM) should be applied in this work.
After that, personalized learning scenarios could be started to create for particular learners for each STEM topic according to curriculum.
Article written by: Virginija Birenniene