在數位遊戲式學習環境中先備能力對兩種學習內容整合方式之影響：緊密結合 vs. 鬆散結合

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論文名稱

在數位遊戲式學習環境中先備能力對兩種學習內容整合方式之影響：緊密結合 vs. 鬆散結合
(The Influences of Prior Ability on Two Approaches for Game-based Learning Content Integration: Tightly-coupling vs. Loosely-coupling)

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摘要(中)

許多研究指出數位遊戲式學習(digital game-based learning, DGBL)可提升學習成效及促進學習動機。此乃是因為DGBL整合了學習內容和遊戲元素，整合的方式有緊密結合DGBL情境(tightly-coupling context, TCC)與鬆散結合的DGBL學習情境(loosely-coupling context, LCC)兩種。在TCC中，學習內容緊密融入遊戲的核心機制，有利情境學習，但認知負荷較重。在LCC中，學習內容和遊戲的核心機制為分離的，因此認知負荷較輕，但可能較缺趣味性。這兩種整合方式各有優缺點，學生的個別差異可能影響他們對這兩種整合方式之優缺點的反應，特別是學生的先備能力，因為它影響到新舊知識的整合。由於此影響，從先備能力的角度來探討學習者對此兩種整合方式之不同反應是有其必要性。有鑑於此，本研究之目的旨在探討此問題。
依前述研究目的，本研究設計一個角色扮演遊戲IEV_RPG (Integrative English Vocabulary RPG, IEV_RPG)，IEV_RPG的學習內容著重在英語字彙，並內含TCC與LCC兩種字彙學習情境。TCC傾向非刻意學習設計(incidental learning)，LCC則傾向刻意學習設計(intentional learning)。在TCC中，英語字彙整合在IEV_RPG的任務對話框內，LCC則以字卡方式呈現英語字彙的拼字與中文翻譯。前述的TCC和LCC被用以進行兩個實驗的實證研究，受試者皆為53位小學五年級學生。實驗一從先備能力探討兩種不同學習內容整合情境(TCC/LCC) 對學習成效之影響，蒐集受試者前測和後測的成績做分析。此外，也藉由訪談來了解學生與此二種學習內容整合情境的互動狀況。實驗一的結果顯示，在LCC的學習成效上，低先備能力學生(Low prior ability students, LAS)與高先備能力學生(High prior ability students, HAS)皆有顯著進步。另外HAS的表現顯著優於LAS。從訪談的資料得知，LAS以看中文翻譯為主，而HAS則能進一步注意英文字母拼字，因此在LCC中LAS的學習成效不如HAS。在TCC的學習成效上， HAS有顯著進步，而LAS沒有顯著進步。訪談資料顯示LAS遇到不認識的字彙傾向採用猜測字義策略，HAS則傾向採用推測字義策略。這可能是造成LAS在TCC沒有顯著進步的原因。然而在TCC中LAS和HAS之間並沒有顯著差異，此外，HAS在LCC學習成效優於其在TCC的表現。實驗一結果顯示，TCC對LAS是困難的，在另一方面，TCC對HAS也是相對困難的。
為了解TCC困難所在，本研究進行實驗二。實驗二採用問卷的方式來進一步明瞭造成他們困難的原因。從問卷的結果得知LAS認為要看懂任務對話框之英文敘述有困難，因此任務對話框的閱讀困難是TCC對LAS困難的原因。在另一方面，問卷結果顯示HAS對TCC戰鬥任務的投入不如LAS，這可能是由於HAS在TCC中不容易同時兼顧學習和遊戲所致，因此TCC對HAS是相對困難的。
本研究有兩個主要貢獻，其一，揭示學生之先備能力對他們在不同學習內容整合情境的學習成效上有很大的影響，也就是HAS及LAS在LCC的學習成效有顯著差異，在TCC的學習成效雖沒有顯著差異，但HAS有顯著進步，而LAS卻沒有。其二，找出TCC為何對學習造成困難的原因。特別是TCC對LAS困難是閱讀對話框困難所致，TCC對HAS相對困難則是不易同時兼顧遊戲與學習所致。
根據上述的貢獻本研究另外提出一個設計框架，說明LAS和HAS在此兩種整合情境反應上的差異。綜言之，本研究將有助於研究者、教學者、和設計者深入了解先備能力在不同學習內容整合方式的影響，進而設計符合不同先備能力學生需求的數位遊戲式英語字彙學習環境。

摘要(英)

Digital game-based learning (DGBL) has advantages on improving learning motivation and learning achievement. These advantages are caused by the fact that learning contents and game elements are integrated together. There are two integrative approaches, i.e., tightly-coupling context (TCC) and loosely-coupling context (LCC). The TCC refers to a scenario, where the learning content is integrated into core gaming mechanisms. It is beneficial to situational learning, but it might bring heavy cognitive load. The LCC refers a scenario, where the learning content is independent from core gaming mechanisms. It needs less cognitive resources but there is a lack of joyfulness. In other words, these two integrative approaches have different strengths and weaknesses. Learners’ individual differences may affect how they react to such strengths and weaknesses. In particular, prior abilities may be an influential factor because it influences the integration of new knowledge and existing knowledge. Therefore, it is necessary to examine the effects of these two approaches from a prior ability aspect. Accordingly, the current study aims to investigate this issue.
To achieve this aim, this research developed a role-playing game named the Integrative English Vocabulary RPG (IEV_RPG), which taught English vocabulary. The IEV_RPG contained both the TCC and the LCC versions. The TCC version was supported by the theory of incidental learning while the LCC was inspired by the theory of intentional learning. In the TCC, English vocabulary was embedded into the task dialogue of the IEV_RPG. In the LCC, each vocabulary was presented in a word card, including spelling and Chinese translation.
The aforementioned TCC and LCC were applied to conduct two empirical studies, which 53 elementary students participated in. Study One explored the impacts of the TCC and LCC on students’ learning performance from a prior ability perspective. The data were collected from students′ pre-test and post-test scores. In addition, interviews were used to investigate how students interacted with the TCC and LCC. Regarding the LCC, both low ability students (LAS) and high ability students (HAS) had significant improvements. However, HAS significantly performed better than LAS. On the other hand, the results from the interviews revealed that LAS focused on Chinese translations while HAS could also pay attention to word spelling of unknown words. This might be the reason why LAS did not perform as well as HAS.
Regarding the TCC, HAS had significant improvements in the TCC while LAS did not. According to the results from the interview, the reason might be that HAS inferred the meaning of the unknown words based on the context of the task dialogue whereas LAS made a guess on the meanings of the unknown words. Nevertheless, there was no significant difference between HAS and LAS in the TCC. Additionally, the learning performance of HAS in the LCC was better than that in the TCC. These results suggested that the TCC was difficult for LAS and it was also relatively difficult for HAS.
Therefore, Study Two used a questionnaire to investigate why the TCC was difficult for both LAS and HAS. The results from the questionnaire showed that comprehending the task dialogue was difficult for LAS. This might be the reason why the TCC was difficult for LAS. The results from the questionnaire also indicated that HAS paid less attention to fighting tasks than LAS in the TCC. This might be because learning and playing simultaneously was not easy for HAS. This was a possible reason why the TCC was also relatively difficult for HAS.
This research has two main contributions. The first contribution lied within the fact that that students′ prior abilities had great influences on their learning performance in the two integrative approaches. Significant performance differences between HAS and LAS were found in the LCC while there were not such differences in the TCC, where HAS made significant improvement whereas no significant improvement was found for LAS. The second contribution is to identify why the TCC was difficult for both HAS and LAS. More specifically, LAS felt difficult to comprehend the task dialog while HAS thought that it was difficult to undertake learning and playing simultaneously.
According to these contributions, a framework is proposed to illustrate how HAS and LAS react differently to the two learning content integrative approaches. To sum up, this research could help researchers, educators, and designers to design a DGBL English learning environment which could adapt the needs of students with different prior abilities.

關鍵字(中)

★ 數位遊戲式學習
★ 先備能力
★ 學習內容整合
★ 緊密結合遊戲式學習情境
★ 鬆散結合遊戲式學習情境
★ 字彙學習

關鍵字(英)

★ digital game-based learning
★ prior ability
★ learning content integration
★ tightly-coupling context
★ loosely-coupling context
★ vocabulary learning

論文目次

摘要 i
Abstract iii
Table of Contents ix
List of Figures xiii
List of Tables xv
Chapter 1 Introduction 1
1.1 Background 1
1.2 Organization of the Thesis 4
1.3 Summary 5
Chapter 2 Literature Review 7
2.1 Learning Content Integration in DGBL 7
2.2 Applications of the TCC and LCC 9
2.3 Comparisons of Two Integrative Approaches 11
2.4 Prior Ability 12
2.5 Definitions of Terminology 15
2.6 Summary 16
Chapter 3 Methodology Design 19
3.1 Introduction 19
3.2 Implementation of a RPG for the TCC and the LCC 20
3.3 Participants 27
3.4 Summary 27
Chapter 4 Study One: TCC vs. LCC 29
4.1 Methodology Design 29
4.1.1 Instruments 29
4.1.2 A Pilot Test 31
4.1.3 Experimental Procedures 31
4.1.4 Data Analysis 33
4.2 Results 35
4.2.1 Learning Performance of Low-Ability Students 35
4.2.2 Learning Performance of High-Ability Students 36
4.2.3 Low-Ability Students vs. High-Ability Students 36
4.2.4 Qualitative Reponses from the Interview 37
4.3 Discussions 40
4.3.1 The LCC 40
4.3.2 The TCC 42
Chapter 5 Study Two: The Impacts of the TCC 45
5.1 Methodology Design 45
5.1.1 The Questionnaire 45
5.1.2 Data Analysis 46
5.2 Results and discussions 47
5.2.1 The Usage Experience 47
5.2.2 The Functionality Design 50
5.2.3 Summary 54
Chapter 6 Development of a Framework 57
6.1 Introduction 57
6.2 Summary of the Findings 58
6.3 Generation of a Framework 61
6.3.1 Low-Ability Students vs. High-Ability Students 61
6.3.2 TCC vs. LCC 62
6.3.3 Implications for System Design 63
Chapter 7 Conclusions 67
7.1 Main Conclusions 67
7.2 Contributions 68
7.3 Limitations and Future Work 69
References 71

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指導教授

楊接期陳攸華(Jie Chi Yang Sherry Y. Chen)

審核日期

2021-1-22

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