What are Alternatives to Traditional Performance Rating Cycles and Processes?

The dominant format for performance appraisal systems in large U.S. industrial companies continues to be an objective-based approach such as management by objectives (MBO). Most companies conduct formal performance ratings annually or semi-annually. However, the traditional way of performance rating is receiving more and more doubt. With the development of HR theories, practices and technology, many companies are trying to manage employee performance in new ways

How Do Organizations Assess for Potential, in addition to a Talent Review Process?

Demand for high potential talent has increased dramatically in the past five years as companies have struggled to fill vacancies due to a competitive talent market. Therefore, having a strong talent pipeline is extremely critical to organizations. Most companies evaluate high potential talent based on three criteria: engagement, ability, aspiration to hold successive leadership. But only 53% of organizations are confident in their ability to select and assess the best talent. An efficient tool is necessary to assess and capture the best talent in the organization. These four categories of tools are widely used by industries and have been considered successful and effective when measuring potential leaders: Assessment and development centers External assessment tools Company self-developed assessment methods 360-degree employee feedback assessment To better understand the benefits of using these tools, this summary report will analyze the essence of the tools and provide case studies of top performers in the industry using these methods

Are there any New or Proven Practices in Identifying Team Members with High Potential ?

Are there any new or proven practices in identifying team members with “high potential”? How do we enable managers to identify high-potential employees and what to do with that insight

Generalization Bounds for Representative Domain Adaptation

In this paper, we propose a novel framework to analyze the theoretical properties of the learning process for a representative type of domain adaptation, which combines data from multiple sources and one target (or briefly called representative domain adaptation). In particular, we use the integral probability metric to measure the difference between the distributions of two domains and meanwhile compare it with the H-divergence and the discrepancy distance. We develop the Hoeffding-type, the Bennett-type and the McDiarmid-type deviation inequalities for multiple domains respectively, and then present the symmetrization inequality for representative domain adaptation. Next, we use the derived inequalities to obtain the Hoeffding-type and the Bennett-type generalization bounds respectively, both of which are based on the uniform entropy number. Moreover, we present the generalization bounds based on the Rademacher complexity. Finally, we analyze the asymptotic convergence and the rate of convergence of the learning process for representative domain adaptation. We discuss the factors that affect the asymptotic behavior of the learning process and the numerical experiments support our theoretical findings as well. Meanwhile, we give a comparison with the existing results of domain adaptation and the classical results under the same-distribution assumption.Comment: arXiv admin note: substantial text overlap with arXiv:1304.157

Integrating Technology, Curriculum, and Online Resources: A Multilevel Model Study of Impacts on Science Teachers and Students

This scale-up study investigated the impact of a teacher technology tool (Curriculum Customization Service, CCS), curriculum, and online resources on earth science teachers’ attitudes, beliefs, and practices and on students’ achievement and engagement with science learning. Participants included 73 teachers and over 2,000 ninth-grade students within five public school districts in the western U.S. To assess the impact on teachers, changes between pre- and postsurveys were examined. Results suggest that the CCS tool appeared to significantly increase both teachers’ awareness of other earth science teachers’ practices and teachers’ frequency of using interactive resources in their lesson planning and classroom teaching. A standard multiple regression model was developed. In addition to “District,” “Training condition”(whether or not teachers received CCS training) appeared to predict teachers’ attitudes, beliefs, and practices. Teachers who received CCS training tended to have lower postsurvey scores than their peers who had no CCS training. Overall, usage of the CCS tool tended to be low, and there were differences among school districts. To assess the impact on students, changes were examined between pre- and postsurveys of (1) knowledge assessment and (2) students’ engagement with science learning. Students showed pre- to postsurvey improvements in knowledge assessment, with small to medium effect sizes. A nesting effect (students clustered within teachers) in the Earth’s Dynamic Geosphere (EDG) knowledge assessment was identified and addressed by fitting a two-level hierarchical linear model (HLM). In addition, significant school district differences existed for student post-knowledge assessment scores. On the student engagement questionnaire, students tended to be neutral or to slightly disagree that science learning was important in terms of using science in daily life, stimulating their thinking, discovering science concepts, and satisfying their own curiosity. Students did not appear to change their self-reported engagement level after the intervention. Additionally, three multiple regression models were developed. Factors from the district, teacher, and student levels were identified to predict student post-knowledge assessments and their engagement with science learning. The results provide information to both the research community and practitioners