January 2012 Volume 15 Number 1 - Educational Technology ...

Feedback on Learning
The learning feedback process involves three entities, namely, the learning feedback agent, the degree of vocabulary
cognition calculation and the memory cycle calculation. The learning feedback agent obtains learning feedback from
a learner and sends it for degree of vocabulary cognition calculation, which computes an ability value representing
the current English vocabulary ability of the learner according to the feedback received. The learning feedback agent
also triggers the memory cycle calculation to re-compute and update the memory cycles of the learned English
vocabulary for an individual learner.
Learning Feedback Scales
In this study, a five-point Likert scale is used to indicate the extent to which a learner understands a recommended
article, where 0.1, 0.3, 0.5, 0.7, and 0.9 indicate “Not at All”, “Not All”, “Moderately”, “Well”, and “Very Well”,
respectively. Learner feedback helps to compute these values and also triggers the degree of vocabulary cognition
calculations and memory cycle calculations.
Degree of Vocabulary Cognition Calculations
To continue presenting appropriate English articles to a learner, the system continuously monitors the English
vocabulary ability of the learner by using the learning feedback collected. The Degree of Vocabulary Cognition is
updated by using formula 1.
new old feedbacki,
d
DVCi, k DVCi,
k
, k
Articled
(1)
C
new
where DVC i,k is the new Degree of Vocabulary Cognition of word k for learner i after reading an article d, and
old
DVC i,
k is the original DVC of word k for the same learner i. feedbacki,d represents the learning feedback that learner
i gives after reading an article d, and C is a constant set to 20 (Saragi, Nation, & Meister, 1978).
Memory Cycle Calculation
The Memory Cycle Calculation computes and updates the memory cycles of the words that have been learned by a
learner. In addition to using the formulae for adjusting memory cycles for vocabulary adapted from Chen and Chung
(2008b), this research infers the relationships between words in an article and intelligently adjusts the memory cycles
of those words that do not appear in immediate tests but are known to a learner. If a word appears in a recommended
article, its memory cycle is updated using formula 2 derived from Chen and Chung (2008b).
i,
w
WTt
feedbacki,
d
i,
w i,
w
MC
d
t MCt
Ft
DVC 1
1
1
i,
w
i,
w
WTt
i w
where MC t
,
1
(2)
i w
stands for the new memory cycle of the English word w for learner i, and MC t
, stands for the
previous memory cycle of the English word w for learner i. The memory cycle of a newly-learned word is initialized
i w
to zero in this study. Ft+1 represents the Fibonacci sequence, and WT t
,
is the number of recommended articles that
contain word w on a particular day t for learner i. DVC i,
w represents the Degree of Vocabulary Cognition of the
English word w for learner i.
Article Recommendation
This study also maintains calculations on article topic preferences in learner portfolios. For intensive reading, fuzzy
logic-based inference is used to find articles of a suitable difficulty level for a given learner. For each learner, the
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