Digital Learning Ecosystem Research Articles

Introducing Tri�Factor Analysis: A Model and Statistical Test of Performance, Efficacy, and Content For Electronics and Digital Learning Ecosystems

This monograph provides an epistemological rational for the novel “Tri–Factor Analysis” statistical model and metric. Thisnew statistic is an innovative and in–depth way of investigating the overall effectiveness of teaching and learning in digitallearning environments and Ecosystems. Tri–Factor Analysis is grounded in the Eduscience (Osler, 2013a) Tri–Factors of:Performance, Efficacy, and Content. Along with the Tri–Squared Test (first introduced in the Journal on Mathematics) it is amathematically–grounded analytical methodology designed to provide educators with quantitative tools, metrics, andanalytics that aid in pedagogical (and andragogical) investigation. The use of the Tri–Factor Analysis methodology alsoadds value to educational inquiry that has been formatted as an eduscientifically–engineered research design thatinvolves the critical analysis of overall classroom and individual learning.

Trichotomous Exploratory Data Analysis [Tri�EDA]: A Post Hoc Visual Statistical Cumulative Data Analysis Instrument Designed to Present the Outcomes of Trichotomous Investigative Models

This paper presents an innovative digital instrument, that uses the novel “Trichotomous Exploratory Data Analysis” [or“Tri–EDA”] as an alternative or supportive research model for traditional confirmatory and probability-based Bayesianstatistical analyses. This research adds to the publication entitled, “Introducing Tri–Factor Analysis: A Model and StatisticalTest of Performance, Efficacy, and Content for Electronics and Digital Learning Ecosystems” published in i-manager’sJournal on Electronics Engineering. This narrative provides an epistemological rational for the use of “Exploratory DataAnalysis” statistical analytical models for the in–depth analysis of the transformative process of qualitative data intoquantitative outcomes through the Tri–Squared Test first introduced in i-manager’s Journal on Mathematics, and furtherdetailed in i-manager’s Journal on Educational Technology, Journal on School Educational Technology, and in Journalon Educational Psychology. Tri–Exploratory Data Analysis (Tri–EDA) is a series of graphical and visual statistical models thatare a part of the Tri–Squared Calculator © created, designed, and developed by the author to report Tri–Squared Testoutcomes and check the validity and reliability of Tri–Squared Test results. This is a novel approach to advanced statisticalTri–Squared reporting, adds considerable merit and value to the mixed methods approach of the trichotomousresearch design (that intrinsically involves the holistic combination and comparison of qualitative and quantitative dataoutcomes).

Pedagogy-driven design of digital learning ecosystems

In most cases, the traditional Web-based learning management systems (e.g. Moodle, Blackboard) have been designed without any built-in support for a preferred pedagogical model or approach. The proponents of such systems have claimed that this kind of inherent "pedagogical neutrality" is a desirable characteristic for a LMS, as it allows teachers to implement various pedagogical approaches. This study is based on an opposite approach, arguing for designing next-generation online learning platforms - so called digital learning ecosystems - with built-in affordances, which promote and enforce desirable pedagogical beliefs, strategies and learning activity patterns while suppressing others. We propose a conceptual and process model for pedagogy-driven design of online learning environments and illustrate it with a case study on development and implementation process of a digital learning ecosystem based on Dippler platform. We also describe the pedagogical foundation of Dippler that was guided by a combination of four contemporary pedagogical approaches: self-directed learning, competence-based learning, collaborative knowledge building and task-centered instructional design.