Can abstraction be used as a unifying guideline to design intelligent educational systems
Bibliographic record
Abstract
ion appears as a good approach to study educational systems. Firstly, teaching by itself is a complex process, where the educator normally is able to present a given topic in various ways, according to the learners background and goals. Secondly, the domains actually taught vary considerably in range, depending on whether and how they refer to memory, to problem-solving, to behaviours and attitudes, etc. Thirdly, almost all teachable domains vary in complexity, from simple basics to intricate constructs and relatively complex problems to solve. For all these reasons, when a human tutor detects errors or misunderstandings, he usually draws the learners attention on a small subset of the involved knowledge, so that the detected errors and/or misunderstandings can be corrected at the proper abstraction level. 2. Discussion objectives For the discussion, I propose to use abstraction as the unifying guideline for the design of IESs and abstraction levels to formalise this design. Questions to be addressed are: This content downloaded from 207.46.13.76 on Wed, 24 Aug 2016 05:59:29 UTC All use subject to http://about.jstor.org/terms 3 ! How are defined the level(s) at which two modules interact? ! To what extent can educational computer modules be defined only by their specifications, like electrical or electronic circuits are? ! How can be defined the level(s) at which a human teacher and a student interact? ! Can such level(s) be simulated by a computer artefact? Under what conditions? ! Are there different ways to define abstraction, depending on the type of concept, of activity, or of process at hand? Depending on the discussants preferences or interests, such questions can be tackled from two complementary perspectives. One more practical may attempt to define and show how abstraction is or can be used in the design and analysis of various modules or functions of educational systems (see examples in section 5.2, item 4). Another perspective, more theoretical, may consist in defining and formalising some various facets of abstraction, like generalisation, complexity levels, hierarchical organisation of concepts, metalevel descriptions, etc., both within an educational subject domain and in domain-independent studies. Of course, it would be great if the two perspectives were to converge... As a more immediate starting point, I suggest the following approach (although the discussants may elect to proceed otherwise). In a problem-solving domain educational system, one can define four fundamental operating modes (Lelouche & Morin, 1997b), based solely on the students main goal for using the system (either to learn or to assess his learning) and the underlying type of knowledge (either domain knowledge or problem-solving knowledge). Thus we could begin by making explicit the abstraction types and abstraction levels used in these four operating modes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".