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題名：自然語言處理應用於興趣評量：分析策略與操作模型
作者：屠珺楠
作者(外文)：TU, JUN-NAN
校院名稱：輔仁大學
系所名稱：心理學系
指導教授：王思峯
學位類別：博士
出版日期：2023
主題關鍵詞：Holland興趣；自然語言處理；特徵提取；機器學習；RAM理論；Holland interest；natural language processing；feature extraction；machine learning；RAM theory
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文本圍繞「自然語言處理在興趣評量領域的應用」展開論述，採用「著作彙編」（Thesis by Publication）形式，以三個研究詳細討論分析策略和操作模型。
研究一：本文嘗試通過兩個研究來探索：當遇到數位原住民（即1980年代以後出生且成長在數位環境之中的現代人）之時，興趣評估與詮釋應當有所不同。研究一設計了「興趣事例」與「規律閱讀」兩道訪題，對屬於數位原住民的208位中國大學生進行量表和訪題的調查，並計算興趣量表何倫碼與訪題文本何倫碼之間的剖面相關，以及使用自然語言處理之詞頻分析和主題建模方法進行訪題文本分析。剖面相關的結果顯示，興趣事例訪題與規律閱讀訪題文本與量表的正強相關人數分別是45人（佔22.28%）與49人（佔26.49%）。該結果對應Barclay等人（2012, 2019）的研究，說明使用訪題評估數位原住民的興趣存在一定風險。自然語言處理的分析結果進一步探索了造成評估風險的原因，即（1）各訪題之間的語言特徵存在差異；（2）數位原住民傾向於使用實體興趣來回答興趣事例題，而使用網絡興趣來回答規律閱讀訪題；（3）網絡領域興趣與實體領域興趣的語言特徵存在差異。研究二增加了「學校科目」訪題，並將樣本量擴大到1304位大學生，在使用自然語言處理方法對訪題文本進行分析之後，也復現了研究一的結果。本文認為，對於興趣內涵的詮釋，固然可以使用傳統生涯領域的理論概念（如特質論、建構論等），但教育領域的情境興趣（Hidi & Renninger, 2006）與資訊領域的行為主題（Sendi et al., 2019）等詮釋或許更加貼近數位原住民的學習與生活。
研究二：在後現代思潮影響下，諮商者愈來愈可能採用晤談方法來衡鑑案主的生涯興趣，但興趣衡鑑與量表測量的結果會有差別嗎？在哪些維度差別較大、哪些較小？造成差別的可能來源為何？Barclay與其同儕的研究顯示，興趣衡鑑與量表測量的結果為弱相關、中剖面相關。本研究以208位大學生為樣本進行調查，研究結果亦複現出類似現象：本研究的剖面相關亦為雙峰分布，約四分之一個案在兩種評量方法有高度相似結果，惟整體而言，訪談衡鑑與量表測量的相關係數屬弱相關範疇，A與S維度兩方法亦有顯著相關，E與C亦無顯著相關。除複現Barclay與其同儕的研究外，本研究進一步採用文字分析與內容分析嘗試探索這些差別現象，探索結果一方面支持了Savickas主張的量表測量與晤談衡鑑之評量標的是有所差別的看法，晤談衡鑑的是人們已經外現出興趣，量表測量的則是人們對量表題項清單喜好態度，二者不僅是方法的不同，評量標的也是彼此有異。另一方面，本研究提出差別現象的另一影響來源～一詞多屬性，並指出一詞多屬性在心理學與AI遭逢時的重要性與潛在研究需求。
研究三：AI評量人格已經存在不少文獻，但AI應用於生涯興趣領域的研究尚有空白。本研究收集了來自中國大陸的1257個學生樣本，使用自然語言處理的預訓練模型特徵提取方法，建立了以訪題文本預測Holland興趣的機器學習模型。在比較各種機器學習模型之後，我們分別確認了衡鑑與測量對應的AI基線模型以驗證AI測評的可行性，且在此基線模型之上比較了各種分析策略組合對於評量效果的影響。再者，我們提出了一個基於RAM理論的AI測評操作模型，並評估了自我與他人一致性、他人與他人一致性、行為預測3個核心指標。之後，我們討論了AI評量興趣的4個重要調節變項，即良好的目標、良好的判斷、良好的特質、良好的信息，以及其心理測量學意義。基線模型的研究結果顯示，以訪題文本預測興趣應視為一個線性回歸問題，彈性網絡模型較適合處理此類線性回歸問題。在諸多分析策略組合中，學校科目訪題對應AI模型的表現優於興趣事例題與規律閱讀題，專業工作活動分量表對應AI模型的表現優於課程與校園活動分量表，乾淨文本與訪題文本的餘弦分析策略對於模型具有一定解釋力。在RIASEC六個興趣維度中，R維度的建模效果最佳，其次是I和A維度，S維度表現平平，而C維度表現不佳、E維度表現最差，該結果或許與各維度的信息可見性或社會期望有關。相較於興趣測量及其對應的AI模型，興趣衡鑑及其對應的AI模型表現更佳，即AI更容易學習以他人評估為數值標籤而非以自我報告為數值標籤。本研究還發現，AI模型的評量結果之間呈現出較高的一致性，甚至超過其模仿對象測量與衡鑑的結果一致性，說明以預訓練模型為基礎的人工智慧或許已經具備一定的興趣概念。最後，測量與衡鑑及其對應的AI模型皆能夠較好地命中學生樣本的專業何倫碼，其中衡鑑AI模型的強迫輸入命中率可以達到49.6%；此外，AI評量方法對於衡鑑方法預測生涯效標具有一定的遞增效度。
The paper focuses on "Applying Natural Language Processing on Interest Evaluation" and adopts the form of "Thesis by Publication" to discuss the analysis strategies and operation model in detail with three studies.
Study One: This paper attempts to explore through two studies: when encountering digital natives (i.e. people who were born and raised after the 1980s in a digital environment), the interest assessment and interpretation should be different. In the first study, two interview questions including "interest case" and "regular reading" were designed to conduct research on the scale and interview answer with 208 Chinese college students who are digital natives. The profile correlation between the Holland code of the interest scale and the Holland code of the interview text was calculated, and the word frequency analysis and topic modeling methods of natural language processing were used to analyze the interview text. The results of profile correlation showed that the number of people whose scale interest is positively corelated with examples of interest was 45 (22.28%), while the number of people whose scale interest is positively corelated with regular reading was 49 (26.49%). This result corresponds to the study of Barclay et al. (2012, 2019), which suggests that there are risks in using interviews to assess the interest of digital natives. The analysis results of natural language processing (NLP) further explored the reasons that caused the assessment risk, namely (1) There were differences in language features among the interview text; (2) Digital natives tend to use physical interests to answer the questions on "interest case", and virtual interests to answer the questions of "regular reading"; (3) There are differences between the linguistic features of interests on the internet domain and interests on physical domain. In the second study, "school subject" questions were added and the sample size was expanded to 1304 college students. The results of the first study were replicated after the interview answers were analyzed using natural language processing method. In this paper, it is believed that the interpretation of interest connotation can use the theoretical concepts of traditional career fields (such as trait theory, constructivism, etc.). However, interpretations such as situational interest in education (Hidi & Renninger, 2006) and behavioral themes in information (Sendi et al., 2019) may be more relevant to the learning and living of digital natives.
Study Two: Under the influence of postmodern trends, counselors are increasingly likely to use conversational methods to assess clients' career interests. However, are there differences between interest assessment and scale measurement? Which dimensions have larger differences, and which dimensions have smaller differences? What are the possible causes of these differences? Research by Barclay and her colleagues have shown weak correlations and moderate profile correlations between interest assessment and scale measurement. In this study, a sample of 208 college students was investigated, and similar phenomena were found in the research results. The profile correlations in this study also exhibited a bimodal distribution, with approximately one-fourth of the cases showing highly similar results between the two methods. However, the correlation coefficient between interview assessment and scale measurement was weak. The A and S dimensions showed significant correlations between the two methods, while the E and C dimensions did not exhibit significant correlations. In addition to replicating the research by Barclay and colleagues, this study further employed text analysis and content analysis to explore these differential phenomena. The results partially support Savickas' claim that the two methods are different. Furthermore, another influencing factor, "one word with multiple attributes," is proposed, highlighting its importance and potential research needs in the encountering between psychology and AI. The results of the exploratory analysis support Savickas' claim that there are differences in the appraisal targets between scale measurement and interview assessment. The interview assessment captures the external manifestation of individuals' expressed interests, while the scale measurement measures individuals' preference towards the items of the scale. These differences not only stem from methodological variations but also indicate distinct assessment targets. Furthermore, this study proposes another influencing factor for the differential phenomenon, namely, "one word with multiple attributes," which also highlights the importance and potential research needs of this concept in the encountering of psychology and AI.
Study Three: There has been a lot of literature on AI evaluation of personality, but there is still a gap in the application of AI to career interests. In this study, 1257 student samples from mainland China were collected, and a machine learning model was established to predict Holland's interested personality by using the pre-trained model feature extraction method of natural language processing. After comparing various machine learning models, we separately confirmed the AI baseline models corresponding to the assessment and measurement to verify the feasibility of AI evaluation, and compared the influence of various combinations of analysis strategies on the evaluation effect on top of the baseline models. Furthermore, we propose an AI evaluation operation model based on RAM theory, and evaluate three core indicators: self-others consistency, others-others consistency, and behavior prediction. After that, we discuss four important moderating variables of the interest personality evaluated by AI, namely good goals, good judgment, good traits, good information, and their psychometric significance. The results of the baseline model show that the prediction of interest personality based on question text should be regarded as a linear regression issue, and the elastic network model is more suitable to deal with this kind of linear regression issue. Among many combinations of analysis strategies, the AI model corresponding to “school subject” interview questions performs better than the “interest example” questions and “interest reading” questions; the AI model corresponding to “professional work activity” subscale performs better than “curriculum and campus activity” subscale; the cosine analysis strategy of clean text and interview answer texts can justify to a certain extent for the model. Among the six RIASEC dimensions of interest, the modeling effect of R dimension is the best, followed by I and A dimension, S dimension is mediocre, C dimension is poor, and E dimension is the worst, which may be related to the information visibility or social expectations of each dimension. Compared to the interest measure and its AI model, the interest assessment and its AI model perform better. That is, it is easier for the AI to learn to use other people's assessment as a numerical label rather than self-report as a numerical label. This study also found that the evaluation results of the AI model showed a high consistency, even more than the consistency of the results of measurement and assessment of its imitation object, indicating that the AI based on the pre-training model may have a certain concept of interest personality. Finally, both the measurement and assessment model and their corresponding AI models can hit the professional Holland code of the student sample well, and the forced input hit rate of assessment AI model can reach 49.6%. In addition, the AI evaluation method has a certain degree of incremental validity for the assessment method to predict the career criterion.
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研究二參考文獻

車萬翔、郭江、崔一鳴 （2022）：《全中文自然語言處理：Pre-Trained Model方法最新實戰》。深智數位。[Che, W.-X., Guo, J., Cui, Y.-M. (2022). Total Chinese Natural Language processing: The Pre-Trained Model in Practice. Deep Mind.]
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Savickas, M. L. (2013). Career construction theory and practice. In R. W. Lent & S. D. Brown (Eds.), Career development and counseling: Putting theory and research to work (pp. 147-183). (2nd ed.). Wiley.
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Speer, A. B., Schwendeman, M., Reich, C. C., Tenbrink, A. P., & Siver, S. R. (2019). Investigating the construct validity of performance comments: Creation of the great eight narrative dictionary. Journal of Business and Psychology, 34(6), 747-767. https://doi.org/10.1007/s10869-018-9599-9
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