ArticleComputational intelligence and neuroscience2022
Evaluation Method of Mixed Teaching Efficiency of College Teachers Based on Kirkpatrick Model.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 6 citations in OpenAlex.
- Retracted: Evaluation Method of Mixed Teaching Efficiency of College Teachers Based on Kirkpatrick Model.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-09-27Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/4FA4AFC5A672B7D66F7828DEF7CA31
- Retracted
Authors and funding
4 authors at 1 institution.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Online teaching has become more prevalent in recent years as a result of the process of digitization and adaptation to the times. Classroom instruction is no longer just conducted in person offline. Online education has received a lot of promotion, especially in the wake of the epidemic's effects, and has evolved into a blended teaching model that combines the two modes. The old system of measuring the effectiveness of teaching is no longer reliable or scientific in assessing the new blended teaching model, and it has many flaws. A perfect system for evaluating the effects of blended learning must therefore be built. In order to more accurately assess the teaching impact of the blended teaching model, this study aims to build a more comprehensive system for evaluating blended teaching effects. This paper introduces the Kirkpatrick four-level evaluation model, which plays a significant role in the evaluation field, in order to achieve this goal. The experiments in this study compared the Kirkpatrick model with the AHP, which is frequently used in the evaluation. Online, offline, and blended teaching models were the subject of an experimental investigation. The experimental results demonstrated that each index combination's weights in the Kirkpatrick model and the AHP model of the blended teaching effect evaluation system were fairly close to one another. The evaluation indicators all center on the assessment of practical ability after class, which is consistent with their focus. The innovation evaluation of course papers has the highest weight, and the weight of evaluation indicators is greater than 0.15; the Kirkpatrick model has the lowest weight of the number of platform logins, with a weight value of 0.011, and the evaluation index of the teaching effect evaluation system based on AHP.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.