Presentation Information
[SS-6-01]Implementing "Quality Assurance" in International Education: Designing Educational Interventions Based on Pre-Program Data and AI-Generated Individual Feedback
*Hajime Nishitani1 (1. Hiroshima University/Soka University)
Keywords:
Quality Assurance in International Education,BEVI (Beliefs,Events,and Values Inventory),Pre-Program Assessment and Intervention Design,AI-Generated Individualized Feedback,Data-Driven PDCA in Study Abroad Programs
受講者に求められる 事前の知識・経験等
BEVI（Beliefs, Events, and Values Inventory）に関する基本的な知識があることが望ましいですが、未経験の方も歓迎いたします。
当日のセッションをより深く理解いただくため、事前Zoomセッション（複数回実施予定／任意参加）への参加を推奨します。これまでの実施経験から、事前セッションにご参加することで、当日のディスカッションやデータ分析への理解が大きく深まります。
事前Zoomセッションへの申込みは、以下のフォームよりお願いいたします。日時が決まり次第、登録いただいた方へ個別にご案内いたします。
申込フォーム：https://forms.gle/sumJjqYQ8nnCWNj8A
受講者が受講前に取り組む 事前課題等
必須の事前課題はありません。ただし、当日のセッションをより楽しんでいただくため、希望者向けに以下の機会を用意してあります。
① 個人としてのBEVIトライアル受検（任意・無料）
ご自身でBEVIを受検することで、当日扱う個人レポートおよびグループレポートの尺度データとの比較ができ、理解が一段と深まります。受検方法は、事前Zoomセッションの後に案内をお送りします。
② 自校データとの比較準備（任意・無料）
所属校（または関係するプログラム）の参加者を対象に、Time 1/Time 2のBEVIデータをトライアルベースで取得し、当日の分析に活用することも可能です。所属校に限らずデータ収集も可能ですのでご相談ください。実施人数・期間に制限はありません。なお、BEVIは受検者本人の同意取得プロセスがシステムに組み込まれています。
申込方法
上記いずれも、事前Zoomセッションと同じ申込フォームのコメント欄にご希望をご記入ください。お申込みいただいた順に、こちらから個別にご連絡します。自校データ収集の準備には一定の期間がかかりますので、ご希望の方はお早めにどうぞ。
申込フォーム：https://forms.gle/sumJjqYQ8nnCWNj8A
概要
PurposeQuality assurance in international education cannot be achieved through post-program satisfaction surveys alone. What is needed is a shift toward a "pre-design PDCA" approach—one that identifies participants' beliefs, values, and intercultural competence profiles before the program begins and designs interventions tailored to both group-level and individual-level characteristics. In this session, participants will use BEVI pre-assessment data (T1) as material to comparatively analyze simulated datasets from three student cohorts with distinct profiles. In addition, through real examples of individualized feedback generated by the AI built into the BEVI system (Being Bevi / BB Insight), participants will explore how group-level intervention design and individual-level support can be integrated.Target AudiencePractitioners and researchers involved in the planning, implementation, and evaluation of international education programs. No prior experience with BEVI is required.Program Structure (80 min.)
[Part 1] Problem Statement and Theoretical Framework (25 min.)
The session opens by questioning the limitations of "one-size-fits-all" program design, connecting Transformative Learning Theory (Mezirow, 1997) with BEVI's data-driven approach (Wandschneider et al., 2015/2016). In addition to introducing how to read group-level data through decile profiles and attribute-based disaggregation, the session presents examples of individualized feedback generated by Being Bevi based on each person's 17-scale scores, as well as BB Insight reports that articulate individual strengths and characteristics in narrative form. This introduces a dual-layer perspective for reading pre-program data: "group tendencies" and "individual narratives."
[Part 2] Comparing Three Cohorts + Utilizing Individual Feedback: Intervention Design Workshop Using Simulated Data (35 min.)
Teams analyze the following simulated datasets:
A: General first-year students — Exploring intervention possibilities through the breadth of score variation
B: Study-abroad-aspiring first-year students — Detecting gaps between motivation and competence
C: Highly selective program cohort (e.g., Hiroshima University START Program) — Identifying hidden challenges within a homogeneous high-scoring group
Each dataset is accompanied by anonymized individual profiles (excerpts of Being Bevi AI feedback and BB Insight reports). Teams examine individual characteristics that group-level analysis alone would overlook and propose both group-level pre-program interventions (orientation design, grouping strategies, debriefing planning) and individualized feedback strategies for specific student profiles.
[Part 3] Cross-Team Review and Plenary Discussion (20 min.) Teams share their analyses and intervention proposals, discussing how intervention design changes depending on cohort characteristics and how group-level design and AI-generated individual feedback can be combined to maximize educational impact.
Learning Objectives
-Understand the basic process of reading group characteristics and challenges from pre-assessment data
-Experience, through comparison of three distinct datasets, how intervention design varies with cohort characteristics
-Understand how AI-generated individual feedback (Being Bevi / BB Insight) functions as a complement to group-level analysis-Develop a concrete vision of the significance and practical steps involved in shifting from "post-evaluation" to "pre-design PDCA"
BEVI（Beliefs, Events, and Values Inventory）に関する基本的な知識があることが望ましいですが、未経験の方も歓迎いたします。
当日のセッションをより深く理解いただくため、事前Zoomセッション（複数回実施予定／任意参加）への参加を推奨します。これまでの実施経験から、事前セッションにご参加することで、当日のディスカッションやデータ分析への理解が大きく深まります。
事前Zoomセッションへの申込みは、以下のフォームよりお願いいたします。日時が決まり次第、登録いただいた方へ個別にご案内いたします。
申込フォーム：https://forms.gle/sumJjqYQ8nnCWNj8A
受講者が受講前に取り組む 事前課題等
必須の事前課題はありません。ただし、当日のセッションをより楽しんでいただくため、希望者向けに以下の機会を用意してあります。
① 個人としてのBEVIトライアル受検（任意・無料）
ご自身でBEVIを受検することで、当日扱う個人レポートおよびグループレポートの尺度データとの比較ができ、理解が一段と深まります。受検方法は、事前Zoomセッションの後に案内をお送りします。
② 自校データとの比較準備（任意・無料）
所属校（または関係するプログラム）の参加者を対象に、Time 1/Time 2のBEVIデータをトライアルベースで取得し、当日の分析に活用することも可能です。所属校に限らずデータ収集も可能ですのでご相談ください。実施人数・期間に制限はありません。なお、BEVIは受検者本人の同意取得プロセスがシステムに組み込まれています。
申込方法
上記いずれも、事前Zoomセッションと同じ申込フォームのコメント欄にご希望をご記入ください。お申込みいただいた順に、こちらから個別にご連絡します。自校データ収集の準備には一定の期間がかかりますので、ご希望の方はお早めにどうぞ。
申込フォーム：https://forms.gle/sumJjqYQ8nnCWNj8A
概要
PurposeQuality assurance in international education cannot be achieved through post-program satisfaction surveys alone. What is needed is a shift toward a "pre-design PDCA" approach—one that identifies participants' beliefs, values, and intercultural competence profiles before the program begins and designs interventions tailored to both group-level and individual-level characteristics. In this session, participants will use BEVI pre-assessment data (T1) as material to comparatively analyze simulated datasets from three student cohorts with distinct profiles. In addition, through real examples of individualized feedback generated by the AI built into the BEVI system (Being Bevi / BB Insight), participants will explore how group-level intervention design and individual-level support can be integrated.Target AudiencePractitioners and researchers involved in the planning, implementation, and evaluation of international education programs. No prior experience with BEVI is required.Program Structure (80 min.)
[Part 1] Problem Statement and Theoretical Framework (25 min.)
The session opens by questioning the limitations of "one-size-fits-all" program design, connecting Transformative Learning Theory (Mezirow, 1997) with BEVI's data-driven approach (Wandschneider et al., 2015/2016). In addition to introducing how to read group-level data through decile profiles and attribute-based disaggregation, the session presents examples of individualized feedback generated by Being Bevi based on each person's 17-scale scores, as well as BB Insight reports that articulate individual strengths and characteristics in narrative form. This introduces a dual-layer perspective for reading pre-program data: "group tendencies" and "individual narratives."
[Part 2] Comparing Three Cohorts + Utilizing Individual Feedback: Intervention Design Workshop Using Simulated Data (35 min.)
Teams analyze the following simulated datasets:
A: General first-year students — Exploring intervention possibilities through the breadth of score variation
B: Study-abroad-aspiring first-year students — Detecting gaps between motivation and competence
C: Highly selective program cohort (e.g., Hiroshima University START Program) — Identifying hidden challenges within a homogeneous high-scoring group
Each dataset is accompanied by anonymized individual profiles (excerpts of Being Bevi AI feedback and BB Insight reports). Teams examine individual characteristics that group-level analysis alone would overlook and propose both group-level pre-program interventions (orientation design, grouping strategies, debriefing planning) and individualized feedback strategies for specific student profiles.
[Part 3] Cross-Team Review and Plenary Discussion (20 min.) Teams share their analyses and intervention proposals, discussing how intervention design changes depending on cohort characteristics and how group-level design and AI-generated individual feedback can be combined to maximize educational impact.
Learning Objectives
-Understand the basic process of reading group characteristics and challenges from pre-assessment data
-Experience, through comparison of three distinct datasets, how intervention design varies with cohort characteristics
-Understand how AI-generated individual feedback (Being Bevi / BB Insight) functions as a complement to group-level analysis-Develop a concrete vision of the significance and practical steps involved in shifting from "post-evaluation" to "pre-design PDCA"