數位學習研究的資訊議題與
趨勢
國立臺南大學 黃國禎
數位學習科技系 教授
資訊教育研究所 所長
理工學院 院長
2006/1/7
突破時空限制
提供個別化教學指導
提高學習興趣
促成同學互動
建立完整的教學管理機制
數位學習的優點
數位學習的發展現況
行政院在九十年
NICI( 國家資訊通信發展方案 ) 計
畫中,將數位學習納入「網路社會化」的一環。
國科會通過「數位學習國家型科技計畫」的構想,
預計五年內投入四十億元進行跨部會計畫
「全民數位學習」
「縮減數位落差」
「行動學習載具與輔具─多功能電子書包」
「數位學習網路科學園區」
「前瞻數位學習技術研發」
「數位學習之學習與認知基礎研究」
「政策引導與人才培育」
數位學習研究的三大趨勢
導入資訊技術
( 人工智慧、演算法、
資料探勘、物件導向、代理人
) 提昇
系統平台功能的研究
對數位學習標準工具及延伸的探討
對行動學習
(M-Learning) 與普化學習
(U-Learning) 平台與教學策略的探討
將在數位學習的研究由應用層次
(Alessi, S.
M. & Trollip, S.R. 1991) 提昇到功能層次
應用層次
利用網路進行教材及試題的共享
利用電腦作為呈現教材及考試的媒介
功能層次
被動
(Chou, C. 1996) :記錄學習過程,統計、保
存、報告學習評量之結果
主動
(Kumar, D.D., Helgeson, S.L. & White, A.L. 1
994) :分析學習狀態、偵查學習迷思、引導學習、
實施有規劃的教學、測驗與評量
個人化學習路徑之規劃
線上學習過程之記錄
線上學習行為之分析
資訊技術應用 1 :
線上學習行為分析
藉由模糊專家系統來推論出此學習者的學習
狀態,並給予適當的幫助
學習效率
(Efficiency of Learning)
學習意願
(Willingness)
耐心度
(Patience)
專心度
(Concentration)
閒置
(Idleness)
理解度
(Comprehension)
聊天
(Chat)
(1) 學習意願分析
學生用心學習的意願
分析依據:有效登入時間 / 登入時間
模糊推理法則
If willingness is low
Then insert INT(T×0.5) corresponding willingness frames.
If willingness is average
Then insert INT(T×0.25) corresponding willingness frames
If willingness is high
Then keep the current status. 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
low average high
degree
(2) 耐心度分析
學生瀏覽一個畫面的持續度
分析依據:畫面學習時間 / 預估學習時間
模糊推理法則
If patience is low
Then record this status and warn the student. If patience is average Then keep the current status. If patience is high
0.2 0.4 0.6 0.8
1 low average high
(3) 專心度分析
學生集中精神於瀏覽教材的程度
分析依據:回應時間
模糊推理法則
If concentration is low Then insert a corresponding concentration frame. If concentration is high Then keep the current status. If concentration is average Then keep the current status.
0 0.2 0.4 0.6 0.8 1 0 0.25 0.5 0.75 1 low no response high RT degree
(4) 聊天狀態分析
學生利用線上討論區來閒聊而不是討論課程
分析依據:學習相關比率
模糊推理法則
If chat is high
Then record this status and warn the student.
If chat is average
Then keep the current status. If chat is low
Then keep the current status 0
0.2 0.4 0.6 0.8
1 high average low
Gwo-Jen Hwang (1998), “A tutoring strategy supporting system for distance l earning on computer networks”, IEEE Transactions on Education, Vol. 41, No . 4, pp. 343. (SCI & EI)
資訊技術應用 2 :
多專家教學知識擷取與整合系統
Computer Networks
Java-based communication unit
Fuzzy Reasoning Interface Expert System Shell Knowledge (Rule) Base Interactive Knowledge Elicitation Unit Knowledge Analysis Unit Tutoring Strategy Negotiation Unit Knowledge Base Generator
模糊專家系統知識擷取
Step 1
: Elicit all of the elements (concepts to be learned)
from the domain expert.
Step 2
: Elicit attributes ( properties or fuzzy
variables).
Li K Fr F Cl I
boiling point LOW;MIDDLE;HIGH atom radius NARROW;NORMAL;WIDE
metalloid WEAK;NORMAL;STRONG negative charge WEAK;MIDDLE;STRONG
Step 3: Fill all of the [concept, attribute] entries of the grid. A 7-scale (-3 to +3) rating and the degree of certainty(“S”,”N”).
Consider the ratings of fuzzy variable ‘boiling point’:
3 means VERY HIGH,
2 means HIGH,
1 means MORE OR LESS HIGH, 0 means MIDDLE,
-1 means MORE OR LESS LOW,
-2 means LOW,
-3 means VERY LOW
‘S’ means ‘VERY SURE’, ‘N’ means ‘NOT VERY SURE’
Li K Fr F Cl I
boiling point -1/N 0/N 1/N 1/S 2/S 3/S LOW;MIDDLE;HIGH atom radius -2/S -1/S 1/N 1/S 2/S 3/S NARROW;NORMAL;WIDE
metalloid 1/S 2/S 3/S -3/S -3/S -3/S WEAK;NORMAL;STRONG negative charge -3/S -3/S -3/S 3/S 2/S 1/S WEAK;MIDDLE;STRONG
Step 4
:
the first column of the above fuzzy table is
translated to the following rule:
IF boiling point is MORE OR LESS LOW, and atom radius is NARROW, and
metalloid is MORE OR LESS STRONG, and
negative charge is VERY WEAK
THEN the element could be Li TRUTH = 0.8
TRUTH
=# " "
(# " " # " ")
.
.
of S
多專家知識擷取與整合的問題
While elicit expertise from multiple experts, some
problems may arise:
same element or attribute different vocabularies Unified vocabularie s Different ratings Conflict happen Insist their ratings
?
??
???
A. Interactive knowledge
elicitation unit
Interactive
user
interface
Fuzzy table
editor
Membership function builder Knowledge base NEGOTIATOR:Please give a set of elements(concepts to be learned or decisions to be made). EDUCATOR: Li, K, Fr, F, Cl, I.
. . .
NEGOTIATOR: Select a set of fuzzy values for fuzzy variable “boiling point”:
1. LOW/MIDDLE/HIGH 2. SHORT/MIDDLE/TALL 3. LIGHT/NORMAL/HEAVY 4. SMALL/MIDDLE/BIG 0. Other (user-defined) EDUCATOR: 1
NEGOTIATOR: Select a set of fuzzy values for fuzzy variable “atom radius”:
1. LOW/MIDDLE/HIGH 2. SHORT/MIDDLE/TALL 3. LIGHT/NORMAL/HEAVY 4. SMALL/MIDDLE/BIG 0. Other (user-defined) EDUCATOR: 0
NEGOTIATOR: Indicate the lower bound of the fuzzy values. EDUCATOR: NARROW
NEGOTIATOR: Indicate the middle of the fuzzy values. EDUCATOR: NORMAL
NEGOTIATOR: Indicates the upper bound of the fuzzy values. EDUCATOR: WIDE
(4) Knowledge analysis unit
Li K Fr F Cl I
boiling point -1/N 0/N 1/N 1/S 2/S 3/S LOW;MIDDLE;HIGH atom radius -2/S -1/S 1/N 1/S 2/S 3/S NARROW;NORMAL;WIDE
metalloid 1/S 2/S 3/S -3/S -3/S -3/S WEAK;NORMAL;STRONG
Check if conflict occurs and integrate tutoring
strategies.
The contents of a fuzzy table is represented as
Fuzzy_value(Educator_ID, Object_name, Fuzzy_variable) and
Certainty_Degree (Educator_ID, Object_name, Fuzzy_variable)
for examples, the fuzzy table below can represented as
Fuzzy_value(Educator1, Li, boiling point) = -1
Certainty_Degree(Educator1, Li, boiling point) = “N” ...
Knowledge analysis rule:
Rule_analysis_02
IF (1)
Current_Phase is Knowledge_Analysis
and
(2) Fuzzy_value(Expi, Gk, Vs)
Fuzzy_value(Expj, Gk,
Vs)
< 0 and
(3) Certainty_Degree (Expi, Gk, Vs)
is
"S"
and
(4) Certainty_Degree(Expj, Gk, Vs)
is
”N”
and
THEN (a) Set
Suggested_Fuzzy_Value
be
Fuzzy_value(Expi, Gk
, Vs)
and
(b) Set
Suggested_Certainty_Degree be ”N"
and
Knowledge analysis rule:
Rule_analysis_04
IF (1)
Current_Phase is Knowledge_Analysis
and
(2) Fuzzy_value(Expi, Gk, Vs)
Fuzzy_value(Expj, Gk, Vs)
0 and
(3) Certainty_Degree (Expi, Gk, Vs)
is
"S"
and
(4) Certainty_Degree(Expj, Gk, Vs)
is
"S”
and
(5) Fuzzy_value(Expi, Gk, Vs)
Fuzzy_value(Expj, Gk, Vs)
0
THEN (a) Set
Suggested_Fuzzy_Value
be
Fuzzy_value(Expi, Gk, Vs)
and
Knowledge analysis rule:
Rule_analysis_03
IF (1)
Current_Phase is Knowledge_Analysis
and
(2) Fuzzy_value(Expi, Gk, Vs)
Fuzzy_value(Expj, G
k, Vs)
< 0 and
(3) Certainty_Degree (Expi, Gk, Vs)
is
"S"
and
(4) Certainty_Degree(Expj, Gk, Vs)
is
"S”
and
THEN (a) Set
Suggested_Fuzzy_Value
be
“Conflict”
and
(b)
Set
Current_Phase
be
Knowledge_Negotiati
on
B. Tutoring Strategy
Negotiation unit
Present suggestions by knowledge analysis unit
When a conflict occurs, experts are asked to give
suggestions.
“
“
over-gener
over-gener
al
al
”
”
ha
ha
ppen
ppen
Bear
Bear
invoke invoke Object_Specialization Object_Specialization procedure procedure An example Check conflict values and decide if Object_Specialization procedure should be invoked Generate fuzzy rules.
(deffacts initial-state
(is boiling-point MORE-OR-LESS LOW) (is atom-radius NARROW)
(is metalloid MORE-OR-LESS STRONG) (is negative-charge VERY WEAK))
(defrule Rule1
?x1 <- (is ?X1 MORE-OR-LESS LOW) ?x2 <- (is ?X2 NARROW)
?x3 <- (is ?X3 MORE-OR-LESS STRONG) ?x4 <- (is ?X4 VERY WEAK)
=>
(retract ?x1 ?x2 ?x3 ?x4) (assert (is Li -1-21-3)) (assert (CF 0.8))
(printout t ”Li is -1-21-3 with CF=0.8" crlf))
C. Knowledge base generator
(export to CLIPS)
Experiment (1): Time for knowledge
elicitation and integration
Unit Group A Time (Hours) Com-pleteness Group B Time (Hours) Com-pleteness Input devices E1, E2 3.8 1 E3, E4 13.1 1 Output devices E1, E3 3.2 0.9 E2, E4 12.5 0.8 Central Processing Unit E1, E4 3.5 0.8 E2, E3 4.5 0.8 Operating systems E2, E3 3.6 1 E1, E4 4.3 1 Network Communica-tion Tools E2, E4 4.2 1 E1, E3 10.6 0.9 Multimedia Systems E , E 5.5 0.8 E , E 8.6 0.85
Experiment (2): Frequency for
correct inferences
250 test cases
Number of
successful inferences
Ratio of successful
inferences
Tutoring Rule Set
by Educator 1
217
0.868
Tutoring Rule Set
by Educator 2
224
0.896
Integrated Tutoring
Rule Set
243
0.972
Gwo-Jen Hwang (2002), “On the Development of a Cooperative Tutoring Env ironment on Computer Networks”, IEEE Transactions on System, Man and C
資訊技術應用 3 :
多目標最佳化配題機制
從大量試題中,選取符合出題條件(題數、測
試時間、概念最低配題比重...等)且鑑別
度最大的試卷
指定測驗時間範圍的試題配置問題模型
(Dedicated Range of Assessment Time Problem-DRAT)
符合期望測驗時間最高界限和最低界限的多目標配
題機制。
固定題數的試題配置問題模型
Fuzzy Art and Dynamic
Programming
Gwo-Jen Hwang (2003), “A Test Sheet Generating Algorithm for Multiple As sessment Requirements”, IEEE Transactions on Education, Vol. 46, No. 3, pp. 329-337. (SCI and EI)
(1) Fuzzy Art:
Classify test items
into groups
(2) Dynamic Programming:
Find optimal test item
composition
階段 n 決策 dn 投入狀態 sn 產出狀態s n+1 報酬函數 rn(sn,dn) 階段 n 決策 dn 投入狀態 sn 產出狀態s n+1 報酬函數 rn(sn,dn)Heuristic Algorithms
FTF (Feasible Time First) Algorithm
Find a solution to meet
range of assessment time
Replace test items to find feasible solutions and to
maximize average discrimination degree
FNTF (Feasible Number of Test Item First)
Algorithm
Find a solution to meet
the number of test items
Replace test items to find feasible solutions and to
Genetic Algorithms
源自於 John Holland 在 1975 年出版的著作 Ad
aptation in Nature and Artificial Systems
仿效自然界生物進化過程
透過基因的選擇 (selection) 交換 (crossover) 及
突變 (mutation) 產生更好的下一代
選擇 (selection) 過程
較高合適值 (fitness value) 就有較大機會獲得保留
較低合適值的解答,可能會遭到淘汰
較不易陷入 local optimal
Population ( 族體 ):
Encoding ( 編碼 ):
Crossover ( 交配 ):
Mutation ( 突變 ):
Selection ( 適者生存 ):
Fitness Function ( 適合度公式 ):
Genetic Algorithm
基因演算法流程圖
基因演算法交配運算
基因演算法突變運算
Crossover randomly selects one-cut-point and
exchanges the right parts of two parents to
generate offspring.
Mutation alters one or more genes with a
probability equal to the mutation rate.
DRAT 目標函式:
Maximize Z =
DRAT 限制式: 第 i 題告第 j 個概念的關係
指定測驗時間範圍的試題配置問題
(Dedicated Range of Assessment Time)
n i 1r
ijx
ih
j,
j
1
,
2
,
,
m
;
n i 1t
ix
il
;
n i 1t
ix
iu
;
n i i n i i ix
x
d
1 1指定概念的最小
出題比重
指定測驗時間的下限
指定測驗時間的上限
指定鑑別度最大化
0
x
1x
2x
3x
4x
98x
99x
1001 1 0
…
0 1 0
DRAT 的試題配置基因演算法 (1
/4)
概念程度下限先決基因演算法
(Concept Lower-bound First Genetic approach – CLFG)
CLFG 建立的母體 (Encoding)
X 為染色體,包含有 n 個基因
X = [x
1, x
2, …, x
n]
X = [0, 0, 1, …, 0]
第 i 個試題被選取時, xi 為 1 ;否則,為 0 ;
= w
dtu ipt_u
dtu =
DRAT 的試題配置基因演算法 (2/
4)
適配等級 (Fitness ranking)
適配函數 v(S
k) =
R =
K
j=
0 if , K
j= 1 else
= w
dtl ipt_l
w = (
ind
ix
i) / average(u, l)
dtl =
j m j n i ij i jr
x
K
h
)
*
(
1 1
n it
x
l
1
n i 1t
ix
iu
n i i n i 1d
ix
i
R
1x
in ij i jr
x
h
1DRAT 的試題配置基因演算法 (3
/4)
交配 (Crossover)
A[1110011001] A’[1110011
011
]
B[0100100011]
B’[0100100
001
]
Procedure: crossover Begin k = 0 while (k ≤ c / 2) do flag = 0 while flag = 0 doGenerate random numbers R1 and R2 from discrete interval [1,K]. If R1 ≠ R2 then flag=1 end while crossover function(R1,R2) end while Cut point
DRAT 的試題配置基因演算法 (4/
4)
突變 (Mutation)
A[11100110
0
1] A’[11100110
1
1]
P = ( 1 / n )
Procedure: mutation Begin for(i=1, i ≤ nk, i++){Generate random number yi from discrete interval [0, 1]. Mutation function(P, yi) }
固定題數的試題配置問題
(Fixed Number of Test Items)
FNTI 目標函式:
Maximize Z =
FNTI 限制式:
x
i≥ 1 Xi 代表題庫中的題號,最小題號為 1
x
i≤
n
最大題號為
n
1 ≤
i
≤
q_num
– 1 共選出
q_num 題
num q id
xi _ 1
num q i j j xh
j
m
r
i _ 1,...,
2
,
1
,
指定鑑別度最大化
指定概念的最小出題比重
12
x
1x
2x
3x
4x
5x
6x
7x
8x
9x
1018 9 45 82 6 2 34 65 71
FNTI 的試題配置基因演算法 (1/
3)
試題數目先決基因演算法
(Feasible Item First Genetic approach – FIFG)
FIFG 的進行步驟
建立母體
X 為染色體,包含有 q_num 個基因
X = [x
1, x
2, …, x
q_num]
X = [25, 118, …., 803]
基因值代表著一題試題的編號
FNTI 的試題配置基因演算法 (2/3)
交配 (Crossover)
A[12,15, 96,112,193,243] A’[12,15,96
,185,256,356
]
B[3,56,108,185,256,356] B’[3,56,108
,112,193,243
]
有兩相同基因值時,隨機更換其中一值,直到沒
有相同基因值為止
試卷中不可有二題相同的試題
Cut pointFNTI 的試題配置基因演算法 (3/
3)
突變 (Mutation)
A[
3,8,56,66,
256
,515
] A’[
3,8,56,66,
346
,515
]
P = ( 1 / n )
Procedure: mutation Begin for (m = 1, m ≤ q_num k, m++){Generate random number rm from discrete interval [0, 1] Generate random number RC from discrete interval [1, n] mutation function(P, rm, RC) }
實驗題庫樣本資料
每一個情況進行二十次實驗處理後,採用平均求解時間和平均
鑑別度建立
實驗樣本
Item
Bank N Loading time(second) DiscriminationAverage
1 25 5.067 0.63267 2 30 5.308 0.65331 3 40 5.217 0.66602 4 250 8.522 0.60985 5 500 8.703 0.60920 6 1000 13.599 0.61208 7 2000 28.361 0.61339 8 4000 60.887 0.61534
CLFG 實驗結果及分析 (1/3)
l = 30
N CLFG Random Selection Optimum Solution Time(sec
) Discrimination Time(sec) Discrimination Time(min) Discrimination 25 0.13275 0.754664 0.03 0.63704 5 0.754664 30 0.14265 0.818120 0.03 0.69388 187 0.818120 40 0.27880 0.880276 0.03 0.64978 163840 0.881440 250 0.96815 0.943386 0.03 0.54248 >106 N/A 500 1.98875 0.952377 0.03 0.60500 N/A N/A 1000 3.75490 0.957359 0.03 0.69753 N/A N/A 2000 7.96650 0.956658 0.03 0.54342 N/A N/A
CLFG 實驗結果及分析 (2/3)
l = 60
N Time(sec) DiscriminationCLFG Time(sec)Random SelectionDiscrimination Time(min) DiscriminationOptimum Solution 30 0.13210 0.707622 0.03 0.64321 187 0.707622 40 0.22985 0.806201 0.03 0.63240 163840 0.806390 250 1.64565 0.924587 0.03 0.62150 >106 N/A 500 2.85260 0.942419 0.03 0.55859 N/A N/A 1000 4.36935 0.950709 0.03 0.63284 N/A N/A 2000 10.12005 0.952650 0.03 0.60746 N/A N/A 4000 27.90960 0.954701 0.03 0.62240 N/A N/A
CLFG 實驗結果及分析 (3/3)
l = 120
N CLFG Random Selection Optimum Solution Time(sec
) Discrimination Time(sec) Discrimination Time(min) Discrimination 250 2.93420 0.896015 0.03 0.59964 >106 N/A
500 4.01775 0.927922 0.03 0.66515 N/A N/A 1000 6.37270 0.940930 0.03 0.62918 N/A N/A 2000 14.80980 0.944458 0.03 0.59838 N/A N/A 4000 35.31320 0.947673 0.03 0.61402 N/A N/A
CLFG 與最佳解的實驗數據圖表
l =
30
Gwo-Jen Hwang, Bertrand M.T. Lin, Hsien-Hao Tseng, Tsung-Liang Lin (20 05), “On the Development of a Computer-Assisted Testing System with Genet ic Test Sheet-Generating Approach”, IEEE Transactions on Systems, Man, a nd Cybernetics: Part C, Vol. 35, No.4, pp. 590-594. (SCI, EI)
其他相關研究議題
Gwo-Jen Hwang, Peng-Yeng Yin and Shu-Heng Yeh (2006)
, “
A Tabu Search Approach to Generating Test Sheets for M
ultiple Assessment Criteria
”, accepted by IEEE Transactions
on Education. (SCI, EI)
Peng-Yeng Yin, Gwo-Jen Hwang, Kuang-Cheng Chang, Gw
o-Haur Hwang and Ying Chan, “
A Particle Swarm Optimiza
tion Approach to Composing Serial Test Sheets for Multiple
資訊技術應用 4 :
學習診斷與導引機制
概念影響關係圖的數學課程範例
Addition of integers Positive integers Multiplication of integers Division of integers Subtraction of integers Negative integers Zero Prime numbersCj C1 C2 C3 C4 C5 C6 C7 C8 Prerequisite Zero Positive integers Addition Subtrac -tion Multipli-c ation Negative integers Division Prime numbers C1 0 0 0 1 0 0 0 0 C2 0 0 1 0 0 0 0 0 C3 0 0 0 1 1 0 0 0 C4 0 0 0 0 0 0 0 0 C5 0 0 0 0 0 0 0 0 C6 0 0 0 0 0 1 1 0 C7 0 0 0 0 0 0 0 1 Ci C8 0 0 0 0 0 0 0 0 NPj 0 0 1 2 1 1 2 1
Conceptual effect table (CET)
概念影響關係表
Test item relationship table (TIRT)
試題概念關係表
Concept
C
jPrerequisite
C1 C2 C3 C4 C5 C^ C7 C8 Q1 1 0.2 0 0 0 0 0 0 Q2 0 0.8 0.4 0 0 0 0 0 Q3 0 0 0.6 0.2 0 0 0 0 Q4 0 0 0 1 0 0 0 0 Q5 0 0 0 0 0 0 0 0 Q6 0.2 0 0 0 0.8 0.2 0 0 Q7 0 0 0 0 0 1 0 0 Q8 0 0 0 0 0 0 0.6 0.4 Q9 0 0 0 0 0.2 0 0 0 Test itemQ
i Q10 0 0 0 0 0.2 0 0.4 1Student answer sheet table (AST)
學生答題狀況表
Test item Student Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 S1 0 0 1 0 0 1 1 0 0 0 S2 0 1 1 0 0 1 1 0 0 0 S3 0 0 0 1 0 1 1 0 0 0 S4 0 1 1 1 0 0 1 0 0 0 S5 0 0 1 0 0 0 1 1 0 0O: The student has correctly answered the test item
1: The student failed to correctly answer the test item
建立補救學習路徑
C
1C
3C
6C
1C
3C
7C
1C
4C
8C
10C
2C
4C
8C
10 0.16 0.6 0.66 0.16 0.6 1 0.16 0.16 0 0 0 0.16 0 0C
2C
5C
9 0 0.28 0假設
θ
值為
0.3 ,代
表我們對於某個概念
之錯誤比率的最大容
忍程度為
30 ,依
上述原則,可得到補
救學習路徑:
PATH1 : C
3C
6PATH2 : C
3C
7歸納式概念影響關係演算法
歸納式概念關係演算法產生的概念繼承關係圖
(Support = 0.1 & Belief=0.7 數學科 )
加法
角度
直角
分數
整數
分子
倍數
除法
乘數
乘法
0.78 0.7 1 0.7 0.7 1 0.7 0.84 1 0.84 0.92 0.89 0.89 0.73加法
角度
直角
分數
整數
分子
倍數
除法
乘數
乘法
0.78 0.7 1 0.7 0.7 1 0.7 0.84 1 0.84 0.92 0.89 0.89 0.73教師修改後的概念繼承關係圖
(Support = 0.1 & Belief=0.7 數學科 )
加法
角度
直角
整數
倍數
除法
乘數
乘法
0.78 0.7 1 0.7 0.7 1 0.7 0.84 0.92 0.89加法
角度
直角
整數
倍數
除法
乘數
乘法
0.78 0.7 1 0.7 0.7 1 0.7 0.84 0.92 0.89其他資訊技術在數位學習的應用
Gwo-Jen Hwang (2006), “
A Gray Forecast Approach for De
veloping Testing and Diagnostic Systems
”, accepted by
IEE
E Transactions on System, man and Cybernetic Part C
. (SC
I and EI)
Gwo-Jen Hwang, Tong C.K. Huang and Judy C.R. Tseng (2
004), “
A Group-Decision Approach for Evaluating Education
al Web Sites
”,
Computers & Education
, Vol. 42, No. 1, pp.
65-86. (SSCI)
黃國禎、陳佐霖、王姿婷、曾秋蓉、黃國豪
(2005) “
線上
自律學習輔助系統之研究與實證
”
, 投稿 科學教育學刊
黃國豪、曾秋蓉、黃國禎、黃繼緯、林農堯
, “
具自我調適
PART B – 數位學習標準及工
具
教材標準及工具的研究 (eg. SCORM)
測驗標準及工具的研究 (eg. QTI)
學習歷程標準及工具的研究
學習設計標準及工具的研究
The 2006 International Conference on SCOR
M (SCORM'2006)
Tamkang University, Taipei, Taiwan
January 17 - 19, 2006
PART C – 行動學習與普化學
習
記錄學生在實境學習環境中的學習歷程,分析學
生即時解決實境問題的能力,並規劃學習活動。
提出教學活動的設計策略,提高學生實境探索、
分析、解決問題的能力,並促成同儕合作。
建立教學元件及學習歷程標準規範與管理策略。
IEEE SUTC2006 International Workshop on Cont
ext Aware Ubiquitous Learning
(CAUL2006)
June 5, 2006, Taichung, Taiwan
數位學習研究的實驗設計
比較答案的品質及演算法的效率 - 以大量資料模擬
測試
瞭解系統的滿意度及使用意願 - 問卷調查
驗證系統或方法的效果 - 以實驗組及對照組進行
3-6 個月的測試及分析 ( 前測及後測 )
比較新系統 ( 方法 ) 與舊系統 ( 方法 ) 的效果 - 多
人交叉使用兩系統 ( 方法 ) 並比較結果
比較對象:舊系統 ( 方法 ) 、使用與未使用、 Rand
投稿的的期刊
國內期刊
如資訊管理學報 (TSSCI) 、師大學報 (TSSC
I) 、科學教育學刊、科學教育研究
國際期刊