This study investigates whether behavioral and performance indicators derived from a Moodle-based learning management system are associated with university students' depression and anxiety in two undergraduate Computer Engineering courses. Using a quantitative observational design, LMS event logs, academic records, and self-reported Beck Depression Inventory-II and Beck Anxiety Inventory scores from 97 students were integrated. A broad set of behavioral and performance indicators spanning temporal engagement, session structure, deadline-related behavior, page-refresh patterns, and LMS navigation was extracted from raw event logs and analyzed using descriptive statistics, independent-samples t-tests with Benjamini-Hochberg FDR correction, effect sizes, and Spearman correlations; inventory scores were confirmed invariant by sex and academic year. Several indicators were significantly associated with depression and anxiety. Higher depression was associated with shifted temporal activity patterns, longer session durations, and shorter homework submission lead times, while higher anxiety was associated with concentrated temporal engagement and session-based differences. These findings suggest that routine LMS data can provide meaningful behavioral signals related to student well-being and may support earlier educational awareness of students who experience mental-health-related strain. At the same time, such indicators should be interpreted as contextual and non-diagnostic markers rather than as substitutes for clinical assessment.

翻译：本研究考察了基于Moodle的学习管理系统中提取的行为与绩效指标是否与两门计算机工程本科课程大学生的抑郁和焦虑水平存在关联。采用定量观察设计，整合了97名学生的LMS事件日志、学业记录以及自评贝克抑郁量表第二版和贝克焦虑量表得分。从原始事件日志中提取了涵盖时间参与、会话结构、截止日期相关行为、页面刷新模式及LMS导航等广泛的行为与绩效指标，并运用描述性统计、独立样本t检验（经Benjamini-Hochberg FDR校正）、效应量及Spearman相关性进行分析；量表得分经检验在性别与年级间无显著差异。多项指标与抑郁和焦虑显著相关：较高抑郁水平与时间活动模式偏移、会话持续时间延长及作业提交提前量缩短相关；较高焦虑水平则与时间参与集中化及基于会话的差异相关。这些发现表明，常规LMS数据能提供与学生心理健康相关的有意义行为信号，可能有助于早期教育层面对经历心理压力学生的识别。同时，此类指标应被视为情境性、非诊断性的标记，而非临床评估的替代品。