頁籤選單縮合
題名 | 「大數據分析輔助大學輔導模式」之建置規劃:以彰化師大為例=Proposing a Big-Data-Assisted College Psychological Services Model at National Changhua University of Education |
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作者 | 林淑君; 鄧志平; 鄭鈴諭; 黃宗堅; 陳嬿任; 王智弘; 羅家玲; Lin, Shu-chun; Deng, Chi-ping; Cheng, Ling-yu; Huang, Tsung-chain; Chen, Yan-ren; Wang, Chih-hung; Luo, Jia-ling; |
期刊 | 本土諮商心理學學刊 |
出版日期 | 20230900 |
卷期 | 14:3 2023.09[民112.09] |
頁次 | 頁261-301 |
分類號 | 527.4 |
語文 | chi、eng |
關鍵詞 | 大學輔導模式; 大數據分析; College psychological services model; Big data analyses; |
中文摘要 | 隨著人工智慧與大數據技術的日益成熟,精準醫療的概念與架構日漸被運用在不同 的領域。彰化師大(簡稱彰師)學生的輔導需求遠高於《學生輔導法》所規範之諮商人 力所能負荷,如何透過科學的研究歷程,找到精準、有效率的學校輔導模式,以提升全 體學生的心理健康,乃是本研究團隊欲解答的問題。本校 109 學年於校長的指導與支持 下,組成「精準輔導與諮商教師專業社群」,開始進行跨專業領域的合作,並啟動「大數 據分析輔助大學輔導模式」之建置。 本研究提出運用大數據分析輔助,以期建置能提高輔導工作效率與效能之模式。此 模式包括三個部分:診斷與預測、諮商與輔導、預防與增能。 1. 診斷與預測:彰師諮輔中心會對全校學生施以心理健康檢測,規劃將利用大數據分 析檢測結果,提供心理健康促進建議,幫助學生及早瞭解自己的心理健康狀態,並且 依據個人需要尋求諮商或心理健康增能服務。另外,也透過心理健康檢測的大數據 分析結果,篩選出可能的高危險學生,進行訪談和關懷,以落實「早期發現早期處 理」之黃金定律。 2. 諮商與輔導目前彰師諮輔中心已利用大數據分析接受諮商學生的困擾症狀資料,將 諮商個案分為五個類型(自我成長型、中度情緒困擾型、高度情緒困擾型、高危困擾 型和認知困擾型)。未來將深入分析此五個類型學生的諮商進展及效能,以發展出「個 別化諮商」模式。 3. 預防與增能:結合全校學生的心理健康資料和接受諮商學生的困擾症狀資料,進行 大數據分析,以瞭解彰師學生常見的心理衛生議題,提供符合各類型學生的心理健 康增能課程和心理衛生推廣活動之規劃參考,以落實「預防勝於治療」之理想 |
英文摘要 | In Obama's 2015 State of the Union Address, the Precision Medicine Initiative was announced. With the advancement of big data analytic techniques, the principles of precision medicine have been applied to various fields such as education (Wu & Tsai, 2019) and public health (Lin et al., 2017). Even though the number of mental health professionals in our school has met the requirement of Student Guidance and Counseling Act, the number of the counseling appointments is barely met the demand of our students’ counseling need. To address this issue, the Precision Counseling Development Team was formed under the supervision and support of the school President. Our team employs a scientist-practitioner approach to work on a Big- Data-Assisted College Psychological Services (BCPSM). We also seek multidisciplinary cooperation in the hope of initiating precision counseling practices in college campuses. Through big data analyses, three processes to establish BCPSM are proposed: 1. Assessment and Screening: The big data analyses will apply to the annual mental health assessment results of students to provide students health promotion suggestions. The results of the big data analyses are also used to locate those who are at high risk and need mental health interventions. 2. Counseling Intervention: Five types of student clients have been derived from the big data analyses of their counseling data. The intervention process and results of these five types will be monitored and analyzed to develop individualized counseling intervention for the students who seek counseling services. 3. Prevention and Health Promotion: The big data analysis results of mental health data and counseling data will be used to understand the campus’ mental health needs and agendas as the topics for the mental health promotion and prevention outreaches. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。