MEF e-Learning Statement
The MEF eLearning Statement articulates the Meta Existential Framework (MEF) in terms of a philosophy of education and paradigms of eLearning specific to the digital age of learning. MEF eLearning is formulated by principles and objectives of eLearning that are plenipotent to mobilise transformational change.
eLearning Principles
Principle 1: Meta Existential Framework (MEF)
MEF eLearning facilitates a comprehensive and structured educational context predicated on the Meta Existential Framework, the superstructure paradigm of knowing, and of ‘being in the world’.
Principle 2: Multiple Learning
MEF eLearning systematically contributes to the total cognitive and experiential reservoir. Typologies of multiple learning include, informal learning, self-directed learning, skills and developmental learning, transformational learning and lifelong learning, inter alia. Applied multiple learning leads to improved quality of life (QoL) indicators by means of defined accretions to the human experience such as social capital formation; enhanced citizenship; democratic participation, community health and well-being.
Principle 3: Independent Learning
MEF eLearning is culturally progressive and promulgates the optimal learning model (OLM) of excellence in pedagogy. MEF eLearning embraces attentive design of teaching methods, demonstrations, and guided practice, to facilitate practical and experiential learning activities combined with reflective exercises to target learning skills and strategies that promote self-directed, independent learning.
Epistemology of eLearning Objectives
MEF eLearning is innately articulated by critical factors in learning design alignment. Anchored in education modalities of experiential learning and creative visualisation, MEF eLearning inculcates a systematic culture of integrated eLearning.
Defined objectives of MEF eLearning include:
(a) Universal Knowledge: informed by the customary knowledge base of the meta-existential framework, MEF eLearning programs inherently conform to the universal ambit of the MEF superstructure
(b) Deep Knowledge: constructive induction to the MEF knowledge base reinforces axiomatic keys to integrated learning constituted by core sub-routines
of systematic, subjective/reflective, critical and ethical learning
(c) Metacognition: harmonisation of new learning with pre-existing knowledge stimulates cognitive awareness and improved aptitude for continued learning success
MEF e-Learning Programs
MEF eLearning Programs are structured courseware technologies that promote the quality teaching model designed for high quality learning outcomes. Instructional materials are carefully adapted to global standards of teaching, development of curricula, paradigms of learning, pupil motivation and assessment. Moreover, MEF eLearning methodologies are specifically customised to differential global profiles of expertise level and learning style.
(a) General Instruction
MEF general programs provide informal instruction and science educational activities premised on explicit formative methods of teaching and learning. MEF general programs focus on techniques and activities designed to enhance pupil engagement, practice skills, and immersion in foundational concepts of science.
General instruction is focused on:
(i) Experiential Learning: concrete learning experiences that blend extant learning with the acquired schemata of knowledge
(ii) Active Learning: self-directed experimental design for empirical analysis and evaluation
(b) Advanced Instruction
MEF advanced programs provide informal instruction and metacognition of the inter-disciplinary sciences based on summative methods of extended relational and abstract learning. Aligned with high level literature in the sciences, MEF advanced programs require core competency in cognitive academic language proficiency (CALP), and are developed to enhance conceptual knowledge, facilitate complex cognition and rational world views in the natural sciences (social constructivism).
Advanced instruction is based on:
(iii) Abstract Learning: inductive processes of assembling and formulating discrete knowledge into coherent schemata and frames of reference
(iv) Reflective Learning: deductive processes of observation, interpretation, and re-callibration of the established knowledge base