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Technology-enabled active learning environments (TE-ALEs) have attracted considerable research interest, particularly in higher education. However, research shows inconsistent results describing the influence of TE-ALEs toward students’ cognitive learning outcomes. This study was designed to identify high-quality empirical research examining college students’ cognitive learning outcomes and to utilize meta-analysis to determine the overall effectiveness of TE-ALEs. A systematic literature search identified 31 high-quality peer-reviewed journal articles that met the inclusion criteria. Meta-analysis showed that the calculated effect size of TE-ALEs more positively influenced students’ cognitive learning than traditional lecture-based environments. Moderator variable analysis suggested that social context, study design, and sample size were significant factors that influence the effectiveness of TE-ALE. TE-ALEs were found more effective when instructors employed individualized learning contexts as well as when bias was reduced in randomized controlled trials. TE-ALEs were also found to be more effective in small courses rather than in large courses.

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In sequence labeling, previous domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual target domain samples, which may lead to negative transfer results for certain samples. Besides, an important characteristic of sequence labeling tasks is that different elements within a given sample may also have diverse domain relevance, which requires further consideration. To take the multi-level domain relevance discrepancy into account, in this paper, we propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from the source domain model. Experiments on three sequence labeling tasks show that our fine-grained knowledge fusion model outperforms strong baselines and other stateof-the-art sequence labeling domain adaptation methods.