專家與生手的差異在哪裡?以滯後序列分析數位五連方拼圖遊戲的空間行為模式為例

作者:國立臺灣科技大學數位學習與教育研究所鄭海蓮、蘋果芽數位科技股份有限公司陳重年

卷期：67卷第4期
日期：2022年12月
頁碼：105-142
DOI：https://doi.org/10.6209/JORIES.202212_67(4).0004

摘要：
科技的進步促進了研究的設計與分析方法，原本只能依賴質性研究的歷程性議題，藉由行為分析技術，能充分地捕捉質性歷程而使其具象化，方便質性歷程資料的再現、整理、檢視、比較與結果的討論。本研究即以質化與量化資料兼備之混合設計，探討專家與生手在數位五連方拼圖遊戲歷程的線上操作行為模式之差異，研究對象為47位國小四、五年級學生。研究結果顯示，專家組在空間能力測驗的表現都顯著地優於生手組，且在各關的各解平均操作次數皆少於生手組，但在困難關卡中才達顯著差異，簡單關卡則否，並且專家與生手在不同難度關卡的解題歷程中，都展現出不同的序列行為模式。專家能持續監控與評估解題歷程，且於必要時能做出快速且適當的修正，進而減少操作次數，提升解題效能。因此在各類將生手育成專家的訓練需求中，需確認是否已培養生手具備系統性思維，才能持續地監控歷程、環境脈絡關係並評估解決方案，亦即讓生手習得如專家一般的問題解決思維與策略，最終方能展現出專家或趨近於專家的表現行為。本研究結果對於空間能力領域以及任何其他領域欲「促進生手育成專家」的研究，提供教學介入、數位內容、人工智慧輔助教學設計或遊戲式學習在未來研究與應用的設計參考。

關鍵詞：人工智慧輔助教育、專家與生手、遊戲式學習、滯後序列分析、數位五連方拼圖遊戲

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參考文獻：

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Where Can We Find the Differences Between Experts and Novices With Lag Sequential Analysis of Spatial Behavioral Patterns in Digital Pentomino Games

Author: Hi-Lian Jeng (Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology), Chung-Nien Chen (APPLE BUDS, LNC.)

Vol.&No.：Vol. 67, No. 4
Date：December 2022
Pages：105-142
DOI：https://doi.org/10.6209/JORIES.202212_67(4).0004

Abstract：
The rapid advancement of technology has led to vigorous growth in research and the applications of digital learning, including digital game-based learning. Digital game-based learning improves learning achievement (Sung & Hwang, 2013; Sung et al., 2015) and learning motivation (Hao & Lee, 2019; Srisawasdi & Panjaburee, 2019), and it enhances higher-order thinking such as critical thinking (Chang et al., 2019; Hussein et al., 2019) and problem-solving (Hwang et al., 2014; Yang, 2015). Digital game-based learning promotes learning in the form of entertainment, and it has thus attracted considerable attention in learning and instruction recently. Advances in technology have also facilitated research designs and analysis methods. Procedural issues that formerly relied on qualitative research methods can now be fully captured and visualized using learning analytics technology, which facilitates the representation, organization, inspection, comparison, and discussion of procedural data.
Procedural data on learning can be used to better explain or predict end performance. Procedural learning analytics can be used to explain learners’ different end performance; and when learners’ end performances are the same, procedural learning analytics may provide a more detailed and refined explanation of their performances. Procedural learning analytics can also provide useful information for optimizing learning designs and environments to improve learning outcomes (Hwang, Chu et al., 2017). Various learning analytics methods aim for different research purposes and designs. Lag sequential analysis is one such method that has been attended in related research.
Although spatial ability is innate and varies among individuals, it can be enhanced through training and learning (Cherney, 2008; Nazareth et al., 2013; Vander Heyden et al., 2017). Spatial ability is related to mathematics capability (Ke, 2019; Krisztián et al., 2015; Ramirez et al., 2012); future attainment in science, technology, engineering, and mathematics (Kell et al., 2013); and future career choices (Jirout & Newcombe, 2015; Uttal & Cohen, 2012). Spatial ability can be improved through digital game-based learning (Hung et al., 2012; Lin & Chen, 2016; Taylor & Hutton, 2013). Pentomino blocks (referred to as Pentomino) constitute an effective material for spatial ability training. Pentomino jigsaw puzzles promote spatial ability (Yang & Chen, 2010). In Pan and Jeng (2018), players applied problem-solving skills that are related to spatial ability during gameplay; different players (experts and novices) applied different problem-solving thinking and strategies. Consequently, their procedural problem-solving skills and strategies also differed. Experts were more systematic in operation and tended to evaluate their outcomes repeatedly, although necessary actions were quickly completed; therefore, the total task time an expert used would be the same as that of a novice. Jeng et al. (2010) combined Thinking Aloud and Pentomino in a spatial performance test for adult participants and observed that for the average number of operations and average operation time, experts and novices were significantly different in the difficult-and-single-　solution tasks only but not in the simple-and-multiple-solution tasks.
Research comparing expert and novice problem-solving has primarily evaluated quantitative data. The procedural differences between these two types of players in digital game-based learning require further research (Loh et al., 2016). Only Pan and Jeng (2018) employed Mining Sequential Patterns with Time Constraints to analyze the spatial operation behaviors of experts and novices in the Digital Pentomino Game for adult participants.
On the basis of the procedures described by Pan and Jeng (2018) and Jeng et al.’s (2010) manipulation of the Digital Pentomino Game, the present study applied a novel and more detailed approach to determine differences in spatial performance between experts and novices. This study used a mixed-methods research design. In the first stage, the independent t-test was used to analyze the spatial ability test scores and the average number of operations of each level in the Digital Pentomino Game. In the second stage, the Lag Sequential Analysis was used to analyze and visualize the procedural operation differences between the two groups in each game level. This study explored the following research questions:
1. Are there significant differences between experts and novices in their scores on three spatial ability tests?
2. Is there a significant difference between experts and novices in the average number of operations for each level of the game?
3. Are there significant differences between experts and novices in the sequential procedural analysis for each level of the game?
This study adopted the Digital Pentomino Game system developed by Pan and Jeng (2018). The game contains six levels. The first to fifth levels involve tasks of two-piece Pentomino combinations, and the sixth level involves a task of three-piece Pentomino combinations. Each level contains single or multiple solutions. The three spatial ability tests used are outlined as follows:
1. Jeng and Li (2014) Computerized Mental Rotation Test
2. Jeng and Liu (2016) Computerized Mental Rotation Test
3. Jeng and Chen (2013) paper-and-pencil standardized spatial ability test
The study participants were 47 fourth- and fifth-grade children (aged between 10 and 11 years). This age range is a critical period for the development of children’s spatial abilities, and it is also a critical period for the emergence of gender spatial differences. With advancements in science, technology, and education, and changes in children nurturing in recent years, the stable age at which children can undertake computerized measures of mental rotation ability (one of the factors of spatial ability) in geometric cubic form is as young as 10 years, in contrast to 13 years as reported by earlier studies.
The study results revealed that the expert group performed significantly better than the novice group on the three spatial ability tests. The expert group had a lower average number of operations per solution in each game level than did the novice group, but the differences were significant only in difficult levels and not in simple levels. The experts and novices exhibited different sequential behavioral patterns in solving every difficult level and simple level as well. The experts continually monitored and evaluated their problem-solving procedures and made quick and appropriate corrections when necessary, thereby reducing the number of operations and improving problem-solving efficiency. This implies that in training programs aimed at cultivating novices into experts, novices must be trained to think systematically so that they can develop the ability to continually monitor task performance and environmental contexts when evaluating solutions. Specifically, novices should be trained to acquire expert-like thinking and strategies so that they can ultimately perform as experts or close to experts.
The results of this study provide design suggestions for related applications and research in teaching intervention, game-based learning at the critical stage of spatial ability development, digital content design, learning analytics methods, and variables of investigative interests in the spatial field and any other fields that involve cultivating novices into experts.

Keywords：artificial intelligence in education, experts and novices, game-based learning, lag sequential analysis, digital Pentomino game